Identity elucidation of unknown metabolites

ABSTRACT

A method of elucidating the identity of an unknown metabolite comprising measuring amounts of known and unknown metabolites in subjects; associating the unknown metabolite with a specific gene from a gene association study; determining a protein associated with the specific gene and analyzing information for the protein; associating the unknown metabolite with concentrations and/or ratios of other metabolites in subjects; obtaining chemical structural data for the unknown metabolite; and using the information obtained to elucidate the identity of the unknown metabolite.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a National Stage application filed under Rule 371 based upon PCT/US12/43461 filed Jun. 21, 2012, which claims the benefit of U.S. Provisional Patent Application No. 61/503,673, filed Jul. 1, 2011, the entire content of which is hereby incorporated by reference.

BACKGROUND

The ability to determine the identity of a chemical entity in a complex mixture has a broad range of highly useful applications. The techniques traditionally used in analysis of complex mixtures include chromatography and mass spectrometry. Although both chromatography and mass spectrometry separate a complex mixture into constituent parts, neither technique provides direct identification of the chemical constituents. Rather, the identity of a chemical constituent must be determined based on an analysis of the measured characteristics of the chemical constituent.

As used herein, the term “identification” as applied to chemical entities refers to the high confidence determination of the identity of a chemical entity. An example of identification is the determination that a molecule having 7 carbon atoms, 7 hydrogen atoms, a nitrogen atom, and 2 oxygen atoms is anthranilic acid rather than salicylamide, both of which have the same chemical formula C₇H₇NO₂.

This ability to perform non-targeted analysis, such as initial detection and subsequent recognition of unknown metabolites, has enormous benefits. For example, in a metabolic analysis of cells with and without cancer, if the analysis results show that cancerous cells almost always contain a certain unknown molecule while healthy cells do not; these results give important direction to research for detection or treatment of that cancer.

Metabolomics includes the ability to perform non-targeted analysis, which means that a chemical constituent may be detected and subsequently recognized, even though it may not be identified.

Currently, methods exist to determine the elemental compositions of ions in a mass spectrum. This knowledge greatly reduces the number of possible compounds that could produce a particular mass spectrum. One can conclusively refute as candidate compounds those that provide similar low resolution mass spectra containing a molecular ion or a fragment ion with a different ion composition. Review of the chemical and commercial literature can further limit the probable identity of an analyte to one or a few compounds. However, in many cases the number of compounds with the same composition is large or the chemical classes of such compounds may represent multiple chemical classes. Thus, even when the list of candidates is reduced to only a few compounds, confirmation is time and resource intensive. In many cases the standards for possible candidates cannot be purchased and instead must be synthesized de novo which can be expensive and time consuming.

Therefore a need exists to improve the ability to elucidate the identity of an unknown compound by narrowing the list of candidate compounds to chemicals from the same biochemical class (e.g., amino acids, fatty acids, carbohydrates) and to further limit the candidates within a particular class.

BRIEF SUMMARY

In an aspect of the invention, a method of elucidating the identity of an unknown metabolite comprises measuring amounts of known and unknown metabolites in subjects; associating an unknown metabolite with a specific gene from a gene association study; determining a protein associated with the specific gene and analyzing information for the protein; associating the unknown metabolite with concentrations and/or ratios of other metabolites in subjects using a partial correlation network; obtaining chemical structural data for the unknown metabolite and deriving from the information obtained the identity of the unknown metabolite.

In a feature, the gene association study may be a genome wide association study. In another feature, the specific gene may comprise a single nucleotide polymorphism. In yet another feature, the method may further comprise reviewing the identity and/or characteristics of the other metabolites associated with the specific gene from the gene association study and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the specific gene are involved.

In an additional feature, the chemical structural data may be obtained using mass spectrometry. The chemical structural data may also be obtained using nuclear magnetic resonance (NMR). The mass spectrometric data of the unknown metabolite may include mass, molecular formula, fragmentation spectra, and retention time. In a further feature, the information concerning the protein known to be associated with the gene may include function of the protein. In another feature, the protein may perform a metabolic function. The protein may be an enzyme. The substrate of the enzyme may be identified.

In another feature, the information for the protein may include the biochemical pathway for the protein substrate. Further, the information may include alternative biochemical pathways for the substrate. An alternative substrate of the enzyme may be determined. In an additional feature, the protein may be a transporter.

In yet another feature, reviewing the identity and/or characteristics of other metabolites associated with the specific gene from the gene association study and/or metabolites associated using the partial correlation network may include reviewing mass, class of compound, retention time, isotope patterns, fragments, and functionality of other metabolites. Further, the association between the protein and the gene may be the protein being encoded by the gene.

In another aspect of the invention, a method of elucidating the identity of an unknown metabolite comprises measuring amounts of known and unknown metabolites in subjects; associating an unknown metabolite with a specific gene from a gene association study; determining a protein associated with the specific gene and analyzing information for the protein; reviewing the identity and/or characteristics of the other metabolites associated with the specific gene from the gene association study; and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the specific gene are involved; obtaining chemical structural data for the unknown metabolite; and deriving from the information obtained the identity of the unknown metabolite.

In yet another aspect of the invention, a method of elucidating the identity of an unknown metabolite comprises measuring amounts of known and unknown metabolites in subjects; associating an unknown metabolite with concentrations and/or ratios of other metabolites in the subjects using a partial correlation network; reviewing the identity and/or characteristics of the other metabolites associated with the unknown metabolite; and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the unknown metabolite are involved; obtaining chemical structural data for the unknown metabolite; and deriving from the information obtained the identity of the unknown metabolite. In a feature of this aspect, the method may further comprise associating the unknown metabolite with a specific gene from a gene association study and determining a protein associated with the specific gene and analyzing information for the protein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application. In the figures:

FIG. 1 is a Manhattan plot demonstrating the locations across the chromosomes of the human genome (on the X-axis) where there was a statistically significant association of the metabolites (knowns and unknowns). In the plot, the higher the dots, the stronger the genetic association.

FIG. 2 is a graphical representation of a Gaussian Graphical Model (GGM) network showing the most significant direct and second neighbors of X-14205, X-4208 and X-14478.

FIG. 3 is a graphical representation of an association network showing the most significant direct and second neighbors of X-11244. Solid lines denote positive partial correlations. Dashed lines indicate negative partial correlations.

FIG. 4 is a graphical representation of an association network showing the most significant direct and second neighbors of X-12441. Solid lines denote positive partial correlations. Dashed lines indicate negative partial correlations.

FIG. 5 is a graphical representation of an association network showing the most significant direct and second neighbors of X-11421. Solid lines denote positive partial correlations. Dashed lines indicate negative partial correlations.

FIG. 6 is a graphical representation of an association network showing the most significant direct and second neighbors of X-13431. Solid lines denote positive partial correlations. Dashed lines indicate negative partial correlations.

FIG. 7 is a graphical representation of an association network showing the most significant direct and second neighbors of X-11793. Solid lines denote positive partial correlations. Dashed lines indicate negative partial correlations.

FIG. 8 is a graphical representation of a GGM network showing the most significant direct and second neighbors of X-11593.

DETAILED DESCRIPTION

The instant invention relates to a method whereby one or a plurality of unknown components (e.g., compounds, molecules, metabolites, biochemicals) can be identified. Biochemical analysis can be performed to aid in determining the identity of the unknown component. Biochemical analysis involves determining an association or relationship between two components (e.g., metabolites) using a correlation network. For example, a first variable showing a significant partial correlation to a second variable may be said to be associated with the second variable. Genetic analysis can also be used to aid in determining the identity of the unknown component. Genetic analysis includes using the association of the unknown component with a genetic locus or a genetic mutation. The association can be made using a genetic association study. A genetic association can be described as the occurrence of two or more traits in association with one another in a population, wherein at least one of the traits is known to be genetic and wherein the association occurs more often than can be explained by random chance. An exemplary genetic association study is a genome wide association study (GWAS). In addition, chemical structural data for the unknown component may be used to aid in determining the identity of the unknown. For example, data obtained from a mass spectrometer, such as accurate mass or ion fragment information, or data obtained from nuclear magnetic resonance may be used.

Information obtained from the biochemical analysis may be used with chemical structural data to aid in elucidating the identity of the unknown component. Information obtained from the genetic analysis may be used with chemical structural data to aid in elucidating the identity of the unknown component. Additionally, information obtained from both biochemical and genetic analysis may be combined and used with chemical structural data to aid in elucidating the identity of the unknown component.

With regard to the genetic analysis, the association of an unknown component with a gene or a genetic polymorphism can reveal the type of reaction in which the unknown component is involved. For example, GWAS analysis between single nucleotide polymorphisms (SNPs) and an unknown component can be used to reveal the type of reaction (for example, methylation) in which the unknown component is involved. As will be understood by one of ordinary skill in the art, the association of an unknown component with a gene or a genetic polymorphism can provide valuable information in determining the identity of the unknown component.

In an exemplary embodiment, metabolic data (for example, the amount of known and unknown metabolites) may be obtained from biological samples taken from subjects in a population group. For the genetic analysis, the metabolic data can be used to associate an unknown metabolite with a genetic locus or a genetic mutation. One of ordinary skill in the art will understand that genotype information for the subjects is also used in making the genetic association. For biochemical analysis, the metabolic data can be used to determine associations between various metabolites using partial correlation networks, which are also called Gaussian Graphical Models (GGMs). Using the GGMs, an association between metabolites represents a partial correlation between the metabolites. A network can be built by drawing connections for metabolites that are associated. The network can provide an estimate for a pathway in which an unknown metabolite is involved.

In an example wherein the genetic association study is a GWAS, results from the biochemical analysis and the GWAS can be combined to aid in determining the identity of the unknown component. In addition to the information obtained from biological samples for the particular subject pool, publicly available metabolic pathway data can also be used to further narrow the list of possible components. Thus, using genetic and biochemical information and publicly available information enables reducing the list of potential components for an unknown component, keeping only those components that play a role in the biochemical context given by the partial correlation network and that could, at the same time, be direct or indirect substrates or products of the specified enzymatic reaction, as determined using the genetic information. Additionally, chemical structural analysis can be performed to aid in determining the identity of the unknown component. For example, mass spectrometry (MS) data (e.g., accurate mass and chemical formula) for the reduced list of potential unknown components can be compared with that of known components to help determine the identity of the unknown component. While the exemplary genetic association study discussed herein is a GWAS, one of ordinary skill in the art will understand and appreciate that the data used in determining the identity of an unknown component can be obtained with other types of genetic association studies.

Genome Wide Association Study

A GWAS is an example of a genetic association study. In a GWAS, a plurality of genes is interrogated for their association with a phenotype. In other types of genetic association studies, the same type of association can be done with a single genetic locus. GWAS have been used to identify hundreds of disease risk loci.

In a GWAS, the density of genetic markers and the extent of linkage disequilibrium are sufficient to capture a large proportion of the common variation in the human genome in the population under study, and the number of specimens genotyped provides sufficient power to detect variants of modest effect. GWAS can be conducted to rapidly and cost-effectively analyze genetic differences between people with specific illnesses, such as diabetes or heart disease, compared to healthy individuals. The studies can explore the connection between specific genes, known as genotype information, and their observable characteristics or traits, known as phenotype information, and can facilitate the identification of genetic risk factors for the development or progression of disease. It will be understood that disease status is an exemplary phenotype. It will also be understood that a GWAS or other genetic association study may be used to analyze data related to any phenotype. Phenotypes can be binary (e.g., diseased or healthy) or can be continuous variable (e.g., BMI, weight, blood pressure). Exemplary continuous variable phenotypes include blood pressure, BMI, height, metabolite concentration, and medication being taken.

The GWAS takes an approach that involves rapidly scanning markers (such as, a genetic polymorphism (for example, a SNP)) across the complete sets of DNA, or genomes, of many people to find genetic variations associated with a particular phenotype (e.g., disease). In the example wherein the phenotype is a disease, once new genetic associations are identified, researchers can use the information to develop better strategies to detect, treat and prevent the disease being studied. Such studies are particularly useful in finding genetic variations that contribute to common, complex diseases, such as asthma, cancer, diabetes, heart disease and mental illnesses. More specific details regarding performing a GWAS will be described below. One of ordinary skill in the art will understand that many of the steps performed in a GWAS are also used in other types of genetic association studies, but typically on a smaller scale because the entire genome is not being scanned.

To carry out a GWAS, researchers characterize the participants by a phenotype (e.g., diseased vs. non-diseased). Researchers obtain DNA from each participant, usually by drawing a blood sample or by rubbing a cotton swab along the inside of the mouth to harvest cells.

Each person's DNA is then purified from the blood or cells, placed on genotyping chips comprised of genetic markers representing the entire genome and scanned on automated laboratory instruments. In a smaller scale genetic association study, a smaller subset of genetic markers would be analyzed. The instruments survey each participant's genome for the presence of markers of genetic variation. A genetic marker is a DNA sequence with a known location on a chromosome with a variation that can be observed. A genetic marker may be a short DNA sequence comprised of a single nucleotide difference or it may be a longer one such as a repeating sequence of DNA or DNA sequence insertions or sequence deletions. The most widely used genetic markers are called single nucleotide polymorphisms, or SNPs. Other types of genetic markers include AFLPs (Amplified Fragment Length Polymorphisms), RFLPs (Restriction Fragment Length Polymorphisms), SSLP (Simple Sequence Length Polymorphisms), RAPDs (Random Amplification of Polymorphic DNA) and CAPS (Cleaved Amplified Polymorphisms).

If certain genetic variations are found to be significantly more frequent or less frequent in people showing a phenotype (e.g., the disease) compared to people lacking this phenotype (e.g., without the disease), the variations are said to be “associated” with the phenotype (e.g., disease). The associated genetic variations can serve as pointers to the region of the human genome where the phenotype-causing problem resides.

The associated variants themselves may not directly cause the disease. They may just be “tagging along” with the actual causal variants. For this reason, researchers often need to take additional steps, such as sequencing DNA base pairs in the particular region of the genome, to identify the exact genetic change involved in the disease.

Genetically determined metabotypes (GDMs) are identified using genetic associations with metabolites measured in biological samples (e.g., blood, urine, tissue) as functional intermediate phenotypes, and facilitate the ability to understand the relevance of these genetic variants for biomedical and pharmaceutical research.

Information obtained using data from a genetic association study can be used for various purposes. For example, the information obtained can be used to associate an unknown biochemical with a SNP and the associated genetic locus. The information can be used to identify an unknown biochemical based upon the function of the protein encoded by the identified gene. The information can be used to associate a known metabolite with the same SNP and locus, which can facilitate identification of biochemical pathways for the unknown biochemical and identification of the unknown biochemical.

Partial Correlation Networks (Gaussian Graphical Models)

Gaussian graphical models (GGMs) are partial correlation networks, which can provide an estimate for the pathway in which an unknown component (e.g., a metabolite) is involved. For example, GGMs can be used to determine metabolic pathway reactions using metabolic concentrations measured for a sample population. Characteristic patterns in metabolite profiles can be directly linked to underlying biochemical reaction networks. In the GGM, a connection between two variables (e.g., metabolites) represents a so-called partial correlation between the variables. A GGM can be represented by drawing metabolite-metabolite connections for pairs of metabolites (knowns or unknowns) that show a significant partial correlation. Connections based on known reaction links between two metabolites based on public metabolic databases can be added to the network representation to provide more identifying information. Considering the neighboring known metabolites of an unknown in the network provides a good estimate for the pathway in which the unknown is involved.

Gaussian graphical models are created using full-order partial correlation coefficients. The partial correlation coefficient between two variables is given by the Pearson correlation coefficient corrected against all remaining (n−2) variables. Intuitively speaking, the partial correlation means that if a pair of metabolites is still correlated after the correction, the correlation is directly determined by the association of the two metabolites and not mediated by other metabolites in the data set. For example, when metabolite A directly affects metabolite B and metabolite B directly affects metabolite C, A and C are also correlated in terms of a non-partial correlation. However, A and C are not correlated after correcting for the correlations between A/B and B/C.

By focusing on direct effects between metabolites, GGMs group metabolites by their biochemical context when applied to targeted metabolomics data. In the present method, a GGM is used with non-targeted metabolomics data containing both known and unknown metabolites. Hence, in order to estimate the biochemical context of an unknown metabolite using the GGM, the context or pathway in which the known metabolites neighboring the unknown metabolite are involved is considered. For facilitating network interpretation, connections based on known reaction links between two metabolites according to metabolic databases such as the KEGG PATHWAY database can be added.

Gaussian graphical models use linear regression models and are able to discern indirect correlations between metabolites that do not indicate an independent association between the metabolites. Any indirect correlations can be removed from the analysis.

In an exemplary embodiment, a method of elucidating the identity of an unknown metabolite comprises measuring amounts of known and unknown metabolites; associating an unknown metabolite with a specific gene from a gene association study; determining a protein associated with the specific gene and analyzing information for the protein; associating the unknown metabolite with concentrations and/or ratios of other metabolites using a partial correlation network; obtaining chemical structural data for the unknown metabolite; and using the information obtained in order to elucidate the identity of the unknown metabolite. Measuring the amounts of known and unknown metabolites comprises analysis a biological sample (e.g., tissue, blood, or urine) to measure the amounts of the metabolites.

EXAMPLES

In order to identify candidate molecules for unidentified molecular entities that were repeatedly observed in MS-based metabolomics measurements, information gained from the application of two different methods on the same population-based sample set was integrated: (i) genome-wide association analysis between single nucleotide polymorphisms (SNPs) and the MS-based quantitative measurements of the aforementioned known and unidentified molecular entities (in this example, the entities are metabolites), and (ii) partial correlation networks (Gaussian Graphical Models) calculated from the quantitative measurements of known as well as unidentified molecular entities (in this example, the entities are metabolites). The study was based on genome-wide SNP data for a population-based cohort and the quantities measured for known and yet unknown molecules by UPLC-MS/MS or GC-MS in blood serum samples from the same cohort. In this study, the population-based cohort was 1768 individuals comprising 859 male and 909 female genotyped individuals, who were aged 32-81 years at the time of sampling.

In the study, over 250 known biochemicals were analyzed in 60 biochemical pathways in 1700+ serum samples. In addition, over 200 unknown biochemicals were quantified in these samples. Metabolic profiling was performed on fasting serum from participants of the study (n=1,768) using ultrahigh performance liquid-phase chromatography and gas chromatography separation coupled with tandem mass spectrometry. Highly efficient profiling (24 minutes/sample) was achieved with low median process variability (12%) of more than 250 metabolites, covering over 60 biochemical pathways of human metabolism.

While the examples describe an approach wherein the entire genome for the subjects was studied, one of ordinary skill in the art will understand that the same type of analysis can be performed for individual genes or individual genetic polymorphisms. Additionally, one of ordinary skill in the art will understand that the sequence of the steps of the analysis process may vary. Such variation is within the scope of the invention.

Genome-Wide Associations

SNP data: Genotyping was carried out using the Affymetrix GeneChip array 6.0. For the analyses, only autosomal SNPs passing the following criteria were considered: call rate >95%, Hardy-Weinberg-Equilibrium p-value p(HWE)>10⁻⁶, minor allele frequency MAF>1%. In total, 655,658 SNPs were left after filtering.

Molecule quantities: The blood serum samples of the 1768 genotyped individuals were screened on known metabolomics platforms (UPLC-MS/MS, GC-MS) providing the relative quantities of (295) known and (224) unknown metabolites in these samples. In order to avoid spurious false positive associations due to small sample sizes, only metabolic traits with at least 300 non-missing values were included and data-points of metabolic traits that lay more than 3 standard deviations off the mean were excluded by setting them to missing in the analysis. 274 known and 212 unknown metabolites passed this filter.

Statistical analysis: The metabolite quantities were log-transformed since a test of normality showed that in most cases the log-transformed distribution was significantly better represented by a normal distribution than when untransformed values were used. The genotypes are represented by 0, 1, and 2 for major allele homozygous, heterozygous, and minor allele homozygous, respectively.

A linear model was employed to test for associations between a SNP and a metabolite assuming an additive mode of inheritance. The tests were carried out using PLINK software (version 1.06) with age and gender as covariates. Based on a conservative Bonferroni correction, associations with p-values <1.6×10⁻¹⁰ meet genome-wide significance. For significant associations of a metabolite (known and unknown) with SNPs within a distance of 10⁶ nucleotides, only the most significant association is reported in Table 1. Table 2 lists all unknown metabolite-SNP associations with p-values below 1×10⁻⁵. Thus, in contrast to Table 1, Table 2 includes (i) associations not reaching genome-wide significance and (ii) all associations rather than only the most significant ones for the 10⁻⁶ nucleotides window.

The SNPs involved in the most significant associations of SNPs and/or the SNPs in the linkage disequilibrium of the association SNPs with known metabolites have shown to be mostly within or close to genes whose function ‘matches’ the metabolite (e.g., association of a SNP in the gene encoding oxoprolinase with oxoproline quantities). This effect can thus be used to narrow the set of candidate molecules in case of unknown metabolites. For example, this effect can be used for estimating the type of enzymatic conversion (or transport) to which an unknown is related. For this purpose, we performed a GWAS on quantities of the unknown (and known) metabolites from the metabolomics data set described above. In case of significant SNP-unknown associations for which the SNP is located close to or within a gene, the genetic information (such as the substrate specificity of the encoded enzyme or transporter) was used as a constraint for reducing the number of candidate molecules. FIG. 1 is a Manhattan plot for the known and unknown metabolites showing a circle for each metabolite-SNP pair for which the p-value of their association is below 1.0×10⁻⁶. The black horizontal line denotes the limit of genome-wide significance. The black triangles represent all associations with p-values lower than 1.0×10⁻³⁰. The associations of unknown metabolites are plotted in the upper part, the associations of known metabolites are plotted in the lower part of the figure. Table 1 provides a list of metabolites and SNPs with which the metabolites were associated most significantly, in particular, within a window of 10⁶ nucleotides. The SNPs are listed along with their position on the genome (CHR: chromosome; Position: position on the chromosome (base pairs)) and the gene that was annotated for the associating SNP or SNPs within the linkage disequilibrium (LD) using an LD criterion of r²>0.8. Moreover, the number of samples is given for which data on the amount of the metabolite and data on the genotype was available. The columns BETA and P (p-value) contain the results of the additive linear model that was used for testing the association between the metabolite and the SNP. Only genome-wide significant associations are shown in Table 1. Table 2 provides a list of all unknown metabolite-SNP associations with p-values below 1×10⁻⁵. Table 3 provides a list of unknown metabolites and shows the metabolite neighbors of the unknown as determined with the GGM network and the best associating SNP for the unknown as determined with the GWAS. Because of size considerations, Tables 2 and 3 are at the end of this description.

TABLE 1 GENE in CHR POSITION SNP LD >0.8 METABOLITE #SAMPLES BETA P 1 47170815 rs1078311 CYP4A11 10-undecenoate (11:1n1) 1744 0.05602 1.47E−013 1 75910194 rs12134854 ACADM hexanoylcarnitine 1732 −0.08111 4.97E−043 1 75910194 rs12134854 ACADM octanoylcarnitine 1735 −0.08345 2.57E−036 1 75910194 rs12134854 ACADM X11421 1721 −0.07391 1.90E−027 1 75879263 rs211718 ACADM decanoylcarnitine 1736 −0.06534 3.77E−021 2 27594741 rs780094 GCKR mannose 1702 −0.04725 2.06E−024 2 73506136 rs7598396 ALMS1 N-acetylornithine 1717 −0.2137 1.30E−149 2 73506136 rs7598396 ALMS1 X12510 1697 −0.1317 1.53E−056 2 73673616 rs6710438 NAT8 X11787 1722 −0.04063 2.95E−037 2 73672444 rs13391552 NAT8 X12093 948 0.1114 8.86E−022 2 210768295 rs2286963 ACADL X13431 1453 0.09672 2.68E−033 2 211325139 rs2216405 CPS1 glycine 1721 0.04127 1.28E−015 2 234333309 rs887829 UGT1A biliverdin 1123 0.1023 5.53E−047 2 234333309 rs887829 UGT1A bilirubin (Z,Z) 1646 0.1551 1.33E−046 2 234337378 rs6742078 UGT1A X11530 1701 0.08613 2.12E−038 2 234337378 rs6742078 UGT1A X11441 1584 0.08834 5.59E−030 2 234333309 rs887829 UGT1A X11793 1539 0.07591 2.59E−026 2 234337378 rs6742078 UGT1A X11442 1584 0.08056 1.19E−025 2 234333309 rs887829 UGT1A bilirubin (E,E) 1694 0.0939 3.04E−024 4 9611763 rs9991278 SLC2A9 urate 1706 −0.02992 4.64E−021 4 22429602 rs358231 GBA3 X11799 1481 0.1373 2.87E−017 4 159850267 rs8396 ETFDH decanoylcarnitine 1711 −0.05031 2.35E−012 4 187394452 rs4253252 KLKB1 bradykinin, des-arg(9) 1463 0.09777 5.93E−014 5 36025563 rs13358334 UGT3A1 X11445 1642 0.08772 2.36E−012 5 131693277 rs272889 SLC22A4 isovalerylcarnitine 1725 0.04099 9.18E−015 5 131689055 rs273913 SLC22A4 3-dehydrocarnitine 1682 0.0298 1.62E−011 6 160589071 rs316020 SLC22A2 X12798 1629 −0.1748 1.73E−072 6 160484466 rs662138 SLC22A1 isobutyrylcarnitine 1700 −0.06786 5.41E−015 7 99078115 rs10242455 CYP3A5 X12063 1660 −0.2485 1.47E−045 7 99327507 rs17277546 CYP3A4 androsterone sulfate 1728 −0.2435 2.08E−021 7 99327507 rs17277546 CYP3A4 epiandrosterone sulfate 1729 −0.1717 3.35E−015 8 18317580 rs1495743 NAT2 1-methylxanthine 1148 −0.09426 6.10E−016 8 145211510 rs6558295 OPLAH 5-oxoproline 1734 −0.0611 8.36E−051 9 135143696 rs651007 ABO ADpSGEGDFXAEGGGVR 1692 0.06719 1.00E−015 10 61119570 rs7094971 SLC16A9 carnitine 1724 −0.02185 1.06E−014 10 85443900 rs12413935 X06226 1722 −0.05911 4.09E−011 10 96454720 rs7896133 CYP2C18 X11787 1738 −0.05619 3.97E−026 10 100149126 rs4488133 PYROXD2 X12092 1711 −0.2842 2.24E−281 10 100149126 rs4488133 PYROXD2 X12093 948 −0.1252 1.35E−027 11 18281722 rs2403254 HPS5 alpha-hydroxyisovalerate 1733 −0.05239 2.60E−016 11 61327924 rs174548 FADS1 arachidonate (20:4n6) 1730 −0.0414 9.98E−022 11 61327359 rs174547 FADS1 1-arachidonoylglycerophosphocholine 1584 −0.05788 2.54E−020 11 61327359 rs174547 FADS1 1-linoleoylglycerophosphoethanolamine 1733 0.04499 1.94E−014 11 61366326 rs174583 FADS1 1-arachidonoylglycerophosphoethanolamine 1699 −0.03864 2.37E−013 11 61327359 rs174547 FADS1 eicosapentaenoate (EPA; 20:5n3) 1730 −0.04007 1.49E−010 12 21222816 rs4149056 SLCO1B1 X11529 1138 0.2564 3.28E−081 12 21222816 rs4149056 SLCO1B1 X11538 1736 0.1087 1.35E−037 12 21222816 rs4149056 SLCO1B1 X13429 1230 0.141 4.86E−022 12 21222816 rs4149056 SLCO1B1 X12063 1660 0.1016 5.22E−020 12 21222816 rs4149056 SLCO1B1 X12456 1260 0.08351 8.41E−017 12 21269288 rs4149081 SLCO1B1 X14626 1235 0.08085 2.08E−013 12 55151605 rs2657879 GLS2 glutamine 1732 −0.01542 3.21E−013 12 119644998 rs2066938 ACADS butyrylcarnitine 1682 0.1983 3.73E−177 15 61209825 rs2652822 LACTB succinylcarnitine 1474 −0.04431 1.05E−021 16 66883701 rs6499165 SLC7A6 glutaroyl carnitine 1675 0.03404 6.49E−011 17 58919763 rs4343 ACE X14189 1703 −0.05975 1.48E−016 17 58923464 rs4351 ACE X14208 1628 −0.05775 4.58E−015 17 58923464 rs4351 ACE X14205 1470 −0.04795 3.97E−014 17 58916932 rs4325 ACE X14304 1467 −0.05359 2.68E−012 17 58916932 rs4325 ACE aspartylphenylalanine 1688 −0.06079 1.05E−011 19 53060346 rs296391 SULT2A1 X11440 1685 −0.1494 1.69E−043 19 53060346 rs296391 SULT2A1 X11244 1676 −0.1464 2.12E−026 22 17352450 rs2023634 PRODH proline 1733 0.05435 4.32E−021 22 18331271 rs4680 COMT X11593 1712 0.04945 1.13E−048 22 18331271 rs4680 COMT X01911 1626 0.07037 5.80E−011 22 23322266 rs5751901 GGT1 cysteine-glutathione disulfide 1598 −0.05311 2.50E−012 Partial Correlation Networks (Gaussian Graphical Models)

In this example, a network was built by drawing metabolite-metabolite connections for pairs of metabolites (knowns or unknowns) that showed a significant partial correlation. To do this network connections based on known reaction links between two metabolites based on public metabolic databases were added. Considering the neighboring known metabolites of an unknown in the network provides a good estimate for the pathway in which the unknown is involved.

The blood serum samples of all 1768 individuals were screened to provide the relative quantities of (295) known and (224) unknown metabolites in the samples. For the calculation of the GGM, the following data preprocessing was applied. All metabolites with more than 20% missing values and all samples with more than 10% missing values were excluded. Remaining missing values were imputed using MICE. MICE stands for Multivariate Imputation by Chained Equations. MICE is a software program used to impute missing values. Multiple imputation is a statistical technique for analyzing incomplete data sets, that is, data sets for which some entries are missing.

Gaussian graphical models were induced by full-order partial correlation coefficients. Additionally, correction was made for SNPs with significant associations to metabolites in the GWAS. Thus, it was expected that the remaining correlations between metabolites were not mediated by metabolite-SNP associations.

By focusing on direct effects between metabolites, GGMs group metabolites by their biochemical context when applied to targeted metabolomics data. In the present method, a GGM is used with non-targeted metabolomics data containing both known and unknown metabolites. Hence, in order to estimate the biochemical context of an unknown metabolite using the GGM, the context or pathway in which the known metabolites neighboring the unknown metabolite are involved is considered. For facilitating network interpretation, connections based on known reaction links between two metabolites according to metabolic databases such as the KEGG PATHWAY database were added.

Gaussian graphical models utilize linear regression models and are thus able to discern indirect correlations between metabolites that do not indicate an independent association between those metabolites and thus remove any indirect correlations from the analysis. If the dataset contained more samples than variables, full-order partial correlations were calculated by a matrix inversion operation. First, regular Pearson product-moment correlation coefficients ρ_(ij) were calculated as:

$P = {\left( \rho_{ij} \right) = \frac{\sum\limits_{k = 1}^{n}{\left( {x_{ki} - {\overset{\_}{x}}_{i}} \right)\left( {x_{kj} - {\overset{\_}{x}}_{j}} \right)}}{\sqrt{\sum\limits_{k = 1}^{n}\left( {x_{ki} - {\overset{\_}{x}}_{i}} \right)^{2}} \cdot \sqrt{\sum\limits_{k = 1}^{n}\left( {x_{kj} - {\overset{\_}{x}}_{j}} \right)^{2}}}}$

Next, partial correlation coefficients were computed as the normalized, negative matrix inverse of this correlation: Z=(ζ_(ij))=−ω_(ij)/√{square root over (ω_(ii)ω_(jj))} with (ω_(ij))=P ⁻¹

P-values p(ζ_(ij)) for each partial correlation were obtained using Fisher's z-transform:

${{z\left( \zeta_{ij} \right)} = {\frac{1}{2}{\ln\left( \frac{1 + \zeta_{ij}}{1 - \zeta_{ij}} \right)}}},{{p\left( \zeta_{ij} \right)} = \left( {1 - {\phi\left( {\sqrt{n - \left( {m - 2} \right) - 3} \cdot {z\left( \zeta_{ij} \right)}} \right)}} \right)}$

where φ stands for the cumulative distribution function of the standard normal distribution. In order to account for multiple hypothesis testing, we applied Bonferroni correction, yielding a corrected significance level of

$\hat{\alpha}:={\frac{0.05}{{n\left( {n - 1} \right)}/2}.}$

Adding connectors from known reactions: Metabolic reactions were imported from three independent human metabolic reconstruction projects: (1) H. sapiens Recon 1 from the BiGG databases (Duarte, et al., 2007), (2) the Edinburgh Human Metabolic Network (EHMN) reconstruction (Ma, et al., 2007) and (3) the KEGG PATHWAY database (Kanehisa & Goto, 2000) as of January 2011.

When adding connectors from known reactions to the GGM, an accurate mapping between the different metabolite identifiers of the respective databases and the identifiers used in the quantitative metabolite data was created. As one of ordinary skill in the art will appreciate, differing forms of biochemical components can represent the same biochemical entity with regard to biochemical pathway. For example, despite the fact that the salt form and the acid form of a metabolite have different names, the salt form of a metabolite will function biochemically the same as the acid form of the metabolite. Accordingly, metabolite identifiers rather than just chemical names are used to create accurate mapping. Database entries referring to whole groups of metabolites, like “phospholipid”, “fatty acid residue” or “proton acceptor” were excluded. Furthermore, metabolic cofactors like “ATP”, “CO₂”, and “SO₄”, etc. were not considered in the analysis, since such metabolites unspecifically participate in a plethora of metabolic reactions.

Combining the GGM and GWAS Results

After the GGM step, a good estimate on the biochemical context of an unknown was obtained. After the GWAS, a good estimate of the enzymatic reaction or transport in which the unknown was directly or indirectly involved was obtained. Once this information was available, it was used to exclude or favor molecules from the list of molecules having a mass that matches a mass measured for the unknown. Additional information provided by mass spectrometry can be used to aid in determining the identity of the unknown. For example, ion fragmentation information can be used. In the following, we demonstrate the procedure by giving two examples.

Example 1

Previously unidentified biomarker X-14205 was identified using the following procedure.

The mass of the unknown X-14205 was determined in a LC/MS/MS run in positive ionization mode. The mass quantified for this unknown was 311.1.

Following the GGM steps described above, a GGM network for X-14205 was obtained. Metabolites shown to have significant partial correlations to X-14205 are listed in Table 4.

TABLE 4 Metabolites having significant partial correlation with the unknown metabolite X-14205. Unknown: Significant partial correlation to: p-value X-14205 X-14478 4.22E−87 DSGEGDFXAEGGGVR (SEQ ID No. 1) 1.89E−35 Cysteine glutathione disulfide 9.59E−33 X-14208 2.25E−24 X-11805 1.49E−17 X-06307 2.24E−16 X-14086 1.34E−12 X-14450 1.73E−12 ADSGEGDFXAEGGGVR (SEQ ID No. 2) 3.29E−09 aspartate 6.52E−09 phenylalanine 3.18E−07 glutamate 4.19E−07 ADpSGEGDFXAEGGGVR (SEQ ID No. 6.26E−07 3)

FIG. 2 provides a GGM network showing the most significant direct and second neighbors of X-14205. Therein, the connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line). In FIG. 2, the starred (*) metabolites denote those that show a significant association with a SNP in the ACE gene.

For X-14205, checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from the unknown. The majority of known metabolites occurring in the GGM of X-14205 are peptides, dipeptides, and amino acids.

In the GWAS analysis, X-14205 was found to associate most significantly with a SNP in the gene encoding the angiotensin I converting enzyme (ACE). This enzyme is known to cut a dipeptide off from the oligopeptide angiotensin I as well as from further oligopeptides. Table 5 shows the most significant hit that was found in the GWAS analysis for X-14205.

TABLE 5 Most significant hit in the GWAS for the unknown X-14205. Unknown Best Assoc. SNP Locus Enzyme p-value X-14205 rs4351 ACE angiotensin I 3.97E−14 converting enzyme

When the results from the GGM and the GWAS were integrated, it appeared that besides X-14205, the dipeptide aspartylphenylalanine and the unknowns X-14208, X-14189, and X-14304, were also significantly associated with SNPs in ACE. (In FIG. 2 all metabolites associating with ACE SNPs are marked by a starred (*) box.) It was hypothesized that X-14205 is a dipeptide (and also X-14208, X-14189, X-14304). Considering the mass of X-14205, the most probable candidates were Glu-Tyr or Tyr-Glu.

In order to experimentally confirm the hypothesis, the accurate mass of X-14205 was determined. Its neutral mass 310.11712 supported the formula C₁₄H₁₈N₂O₆, which also fits the two hypothesized dipeptides. For experimental validation, Glu-Tyr and Tyr-Glu from a commercial source were run on a proprietary LC/MS/MS platform. The retention time and the fragmentation spectrum received for Glu-Tyr matched the time and spectrum of X-14205. Thus, using the above-described method, X-14205 was identified by testing only two candidate molecules.

Example 2

Previously unidentified biomarker X-14208 was identified using the following procedure.

The mass of the unknown X-14208 was determined in a LC/MS/MS run in positive ionization mode. The mass quantified for this unknown was 253.1.

Following the GGM steps described above, a GGM network for X-14208 was obtained. Metabolites shown to have significant partial correlations to X-14208, are listed in Table 6.

TABLE 6 Metabolites having significant partial correlation with the unknown metabolite X-14208. Unknown: Significant partial correlation to: p-value X-14208 X-14478  4.67E−153 X-11805 6.14E−62 X-14205 2.25E−24 X-14086 1.83E−14 lysine 5.68E−11

FIG. 2 provides a GGM network showing the most significant direct and second neighbors of X-14208. Therein, the connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line). In FIG. 2, the starred (*) metabolites denote those that show a significant association with a SNP in the ACE gene.

For X-14208, checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from the unknown. The majority of known metabolites occurring in the GGM of X-14208 are peptides, dipeptides, and amino acids.

In the GWAS analysis, X-14208 was found to associate most significantly with a SNP in the gene encoding the angiotensin I converting enzyme (ACE). This enzyme is known to cut a dipeptide off from the oligopeptide angiotensin I as well as from further oligopeptides. Table 7 shows the most significant hit from the GWAS analysis for X-14208.

TABLE 7 Most significant hit in the GWAS for the unknown X-14208. Unknown Best Assoc. SNP Locus Enzyme p-value X-14208 rs4351 ACE angiotensin I 4.58E−15 converting enzyme (peptidyl- dipeptidase A) 1

When the results from the GGM and the GWAS were integrated, it appeared that besides X-14208, the dipeptide aspartylphenylalanine and the unknowns X-14205, X-14189, and X-14304, were also significantly associated with SNPs in ACE. (In FIG. 2 all metabolites associating with ACE SNPs are marked by a starred (*) box.) It was hypothesized that X-14208 is a dipeptide (and also X-14205, X-14189, X-14304). Considering the mass of X-14208, the most probable candidates were Phe-Ser or Ser-Phe.

In order to experimentally confirm the hypothesis, the accurate mass of X-14208 was determined. Its neutral mass 252.11172 supported the formula C₁₂H₁₆N₂O₄, which also fits the two hypothesized dipeptides. The formula matches more than 1,200 molecular structures, but the prediction of this unknown as a dipeptide narrowed the field to only the two candidate molecules for validation. For experimental validation, Phe-Ser and Ser-Phe from a commercial source were run on a proprietary LC/MS/MS platform. The retention time and the fragmentation spectrum received for Phe-Ser matched the time and spectrum of X-14208. Thus, using the above-described method, X-14208 was identified by testing only two candidate molecules.

Example 3

Previously unidentified biomarker X-14478 was identified using the following procedure.

The mass of the unknown X-14478 was determined in a LC/MS/MS run in positive ionization mode.

Following the GGM steps described above, a GGM network for X-14478 was obtained. Metabolites shown to have significant partial correlations to X-14478, are listed in Table 8.

TABLE 8 Metabolites having significant partial correlation with the unknown metabolite X-14478. Unknown: Significant partial correlation to: p-value X-14478 X-14208  4.67E−153 X-14205 4.22E−87 X-11805 3.95E−55 X-14450 4.67E−14 cysteineglutathionedisulfide 5.67E−14 aspartylphenylalanine 3.51E−10

FIG. 2 provides a GGM network showing the most significant direct and second neighbors of X-14478. Therein, the connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line).

For X-14478, checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from the unknown. The majority of known metabolites occurring in the GGM of X-14478 are peptides, dipeptides, and amino acids.

The GGM network showed partial correlations of X-14478 with peptides, dipeptides and amino acids. It was hypothesized that X-14478 is a peptide, dipeptide or amino acid. Considering the mass of X-14478, the most probable candidate was the dipeptide Phe-Phe.

In order to experimentally confirm the hypothesis, the accurate mass of X-14478 was determined. For experimental validation, Phe-Phe from a commercial source was run on a proprietary LC/MS/MS platform. The retention time and the fragmentation spectrum received for Phe-Phe matched the time and spectrum of X-14478. Thus, using the above-described method, X-14478 was identified by testing only one candidate molecules.

Example 4

Previously unidentified biomarker X-11244 was identified using the following procedure.

The mass of the unknown X-11244 was determined in a LC/MS/MS run in negative ionization mode. The mass quantified for this unknown was 449.1.

Following the GGM steps described above, a GGM network for X-11244 was obtained. Metabolites shown to have significant partial correlations to X-11244, are listed in Table 9.

TABLE 9 Metabolites having significant partial correlation with the unknown metabolite X-11244. Unknown: Significant partial correlation to: p-value X-11244 X-11443  8.93E−113 X-11440 7.62E−93 dehydroisoandrosteronesulfateDHEAS 7.47E−37 epiandrosteronesulfate 6.66E−16 thromboxaneB2 1.12E−12 X-11470 4.12E−09

FIG. 3 provides an illustration of the association network showing the biochemical edges from the GGM, genetic associations from the GWAS, and pathway annotations showing the most significant direct and second neighbors of X-11244. Therein, the connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line).

The majority of known metabolites occurring in the GGM of X-11244 are related to steroid-hormone compounds. Checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from X-11244.

In the GWAS analysis, X-11244 was found to associate most significantly with a SNP in the gene encoding SULT2A1 which is a member of the sulfotransferase family 2A, dehydroepiandrosterone-preferring. Table 10 shows the most significant hit from the GWAS analysis for X-11244.

TABLE 10 Most significant hit in the GWAS for the unknown X-11244. Unknown Best Assoc. SNP Locus Enzyme p-value X-11244 rs296391 SULT2A1; sulfotransferase 2.12E−26 CRX family, cytosol- ic, 2A, dehy- droepiandro- sterone (DHEA)-pre- ferring, mem- ber 1; cone- rod homeobox

When the results from the GGM and the GWAS were integrated, it appeared that besides X-11244, the sulfated steroids related to androsterone and the unknowns X-11440, and X-11443 were also significantly associated with SNPs in SULT2A1. It was hypothesized that X-11244 is a steroid sulfate related to androsterone.

In order to experimentally confirm the hypothesis, the accurate mass of X-11244 was determined. Its neutral mass of 450.13835 supported the formula C₁₉H₃₀O₈S₂. Using LC/MS/MS in negative ionization mode, the primary loss of a fragment with a nominal mass of 98 and the presence of an ion at 97 m/z were observed in the fragmentation spectrum of X-11244 which indicated the presence of at least one sulfate group in X-11244. For experimental validation, several disulfated androstenes from a commercial source were run on a proprietary LC/MS/MS platform. All demonstrated similar retention times and fragmentation spectra. Among the variants that were tested, 4-androsten-3β,17β-disulfate showed the best match to the retention time and fragmentation spectrum of X-11244. Given that other isomers are also possible, which cannot necessarily be chromatographically resolved, X-11244 was annotated more generically as androstene disulfate.

Example 5

Previously unidentified biomarker X-12441 was identified using the following procedure.

The mass of the unknown X-12441 was determined in a LC/MS/MS run in negative ionization mode. The mass quantified for this unknown was 319.2.

Following the GGM steps described above, a GGM network for X-12441 was obtained. Metabolites shown to have significant partial correlations to X-12441 are listed in Table 11.

TABLE 11 Metabolites having significant partial correlation with the unknown metabolite X-12441. Unknown: Significant partial correlation to: p-value X-12441 arachidonate204n6  1.52E−116 @1arachidonoylglycerophosphocholine 9.39E−13 docosahexaenoateDHA226n3 2.74E−09 X-10810 2.26E−07 dihomolinolenate203n3orn6 6.48E−07

FIG. 4 provides an association network showing the most significant direct and second neighbors of X-12441. Therein, the connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line). In FIG. 4, direct pathway interaction is shown between X-12441 and the known metabolite 12-HETE which has the same mass as X-12441.

In the GGM analysis, one GGM neighbor (arachidonate) was found. FIG. 4 shows that arachidonate has pathway connections to several lipid-related metabolites, including a variety of hydroxyl-arachidonate variants (HETEs). These HETE variants have the chemical formula C₂₀H₃₂O₃ and a molecular weight of 320.2351, which matched the mass of X-12441. When the results from the GGM, and pathway analysis were integrated, it was hypothesized that X-12441 was a species of HETE.

In order to experimentally confirm the hypothesis, the accurate mass of X-12441 was determined. Its neutral mass of 320.23430 supported the formula C₂₀H₃₂O₃, which also fits the hypothesis of a species of HETE, as this mass matches the chemical composition of HETE to a precision of +/−0.002 Da. For experimental validation, HETE isoforms 5, 8, 9, 11, 12 and 15 from a commercial source were run on a proprietary LC/MS/MS platform. The retention time and the fragmentation spectrum of the 12-HETE isoform matched the time and spectrum of X-12441. Thus, using the above-described method, X-12441 was identified by testing six HETE isoforms and was identified as 12-HETE.

Example 6

Previously unidentified biomarker X-11421 was identified using the following procedure.

The mass of the unknown X-11421 was determined in a LC/MS/MS run in positive ionization mode.

Following the GGM steps described above, a GGM network for X-11421 was obtained. Metabolites shown to have significant partial correlations to X-11421 are listed in Table 12.

TABLE 12 Metabolites having significant partial correlation with the unknown metabolite X-11421. Unknown: Significant partial correlation to: p-value X-11421 X-13435 9.93E−56 linoleate182n6 1.55E−45 octanoylcarnitine 1.05E−40 hexanoylcarnitine 1.00E−23

FIG. 5 provides an association network showing the most significant direct and second neighbors of X-11421. This network incorporates GGM, GWAS and pathway associations. The connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line).

For X-11421, checking for known reactions from metabolic databases revealed carnitine species as additional connectors within a distance of two from the unknown. The majority of known metabolites occurring in the GGM of X-11421 are carnitine species.

In the GWAS analysis, X-11421 was found to associate most significantly with a SNP in the gene encoding the acyl-coenzyme A dehydrogenase (ACAD) for medium-chain length fatty acyl residues (ACADM). Table 13 shows the most significant hit from the GWAS analysis for X-11421. When the results from the GGM, GWAS and pathway analyses were integrated, it was hypothesized that X-11421 is a medium-chain length carnitine.

TABLE 13 Most significant hit in the GWAS for the unknown X-11421. Unknown Best Assoc. SNP Locus Enzyme p-value X-11421 rs12134854 ACADM acyl-CoA 1.90E−27 dehydrogenase, C-4 to C-12 straight chain

To experimentally confirm the hypothesis generated from the GGM, GWAS and pathway analyses, the accurate mass of X-11421 was determined. The LC/MS/MS analysis experimentally validated the hypothesis since the results showed that the retention time and fragmentation spectrum of X-11421 matched the retention time and fragmentation spectrum of cis-4-decenoyl-carnitine. Thus, using the above-described method, X-11421 was identified as cis-4-decenoyl-carnitine which is a carnitine with 10 carbon atoms and an ω-6 double bond.

Example 7

Previously unidentified biomarker X-13431 was identified using the following procedure.

The mass of the unknown X-13431 was determined in a LC/MS/MS run in positive ionization mode. The mass quantified for this unknown was 302.2.

Following the GGM steps described above, a GGM network for X-13431 was obtained. Metabolites shown to have significant partial correlations to X-13431 are listed in Table 14.

TABLE 14 Metabolites having significant partial correlation with the unknown metabolite X-13431. Unknown: Significant partial correlation to: p-value X-13431 @10undecenoate111n1 1.82E−14 @2methylbutyroylcarnitine 6.68E−13 X-12442 5.43E−12 @1palmitoleoylglycerophosphocholine 2.75E−07

FIG. 6 provides an association network showing the most significant direct and second neighbors of X-13431. This network incorporates GGM and GWAS associations. The connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line).

For X-13431, checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from the unknown. The GGM of X-13431 shows an association with a C11 fatty acid.

In the GWA analysis, X-13431 was found to associate most significantly with a SNP in the gene encoding the acyl-coenzyme A dehydrogenase (ACAD) for long-chain length fatty acyl residues (ACADL). ACADL has been shown to alter C9 carnitines. Table 15 shows the most significant hit from the GWAS analysis for X-13431. When the results from the GGM and GWAS analyses were integrated, it was hypothesized that X-13431 is a C9 carnitine.

TABLE 15 Most significant hit in the GWAS for the unknown X-13431. Unknown Best Assoc. SNP Locus Enzyme p-value X-13431 rs2286963 ACADL acyl-CoA 2.68E−33 dehydrogenase, long chain

In order to experimentally confirm the hypothesis, the accurate mass of X-13431 was determined. Its neutral mass 301.22476 supported the formula C₁₆H₃₁NO₄, which also is consistent with the hypothesis of a C9 carnitine. Exact mass, fragmentation pattern and chromatographic retention time supported the identification of X-13431 as nonanoyl carnitine. Thus, using the above-described method, X-13431 was identified as nonanoyl carnitine.

Example 8

Previously unidentified biomarker X-11793 was identified using the following procedure.

The mass of the unknown X-11793 was determined in a LC/MS/MS run in positive ionization mode. The mass quantified for this unknown was 601.26587.

Following the GGM steps described above, a GGM network for X-11793 was obtained. Metabolites shown to have significant partial correlations to X-11793 are listed in Table 16.

TABLE 16 Metabolites having significant partial correlation with the unknown metabolite X-11793. Unknown: Significant partial correlation to: p-value X-11793 bilirubinEE 8.57E−108

FIG. 7 provides an association network showing the most significant direct and second neighbors of X-11793. This network incorporates GGM and GWAS associations. The connectors connect significantly partially correlating metabolites. The connectors are weighted by the degree of significance (specifically, the lower the p-value of the correlation, the thicker the line).

For X-11793, checking for known reactions from metabolic databases did not provide additional connectors within a distance of two from the unknown. The GGM of X-11793 shows an association with three bilirubin steroisoforms.

In the GWAS analysis, X-11793 was found to associate most significantly with a SNP in the gene encoding the UDP glucuronosyltransferase 1 family, polypeptide A. Table 17 shows the most significant hit from the GWAS analysis for X-11793. When the results from the GGM and GWAS analyses were integrated, it was hypothesized that X-11793 is an oxidized bilirubin variant.

TABLE 17 Most significant hit in the GWAS for the unknown X-11793. Unknown Best Assoc. SNP Locus Enzyme p-value X-11793 rs887829 UGT1A UDP glucuronosyl- 2.59E−26 transferase 1 family, poly- peptide A

In order to experimentally confirm the hypothesis, the accurate mass of X-11793 was determined. Its neutral mass 600.25859 supported the formula C₃₃H₃₆N₄O₇, which also is consistent with the hypothesis of an oxidized bilirubin variant. Exact mass, fragmentation pattern and chromatographic retention time supported to identification of X-11793 as an oxidized bilirubin variant. Thus, using the above-described method, X-11793 was identified as an oxidized bilirubin variant.

Example 9

Previously unidentified biomarker X-11593 was identified using the following procedure.

The mass of the unknown X-11593 was determined in a LC/MS/MS run in negative ionization mode. The mass quantified for this unknown was 189.2.

The GGM for X-11593, including its direct and second neighbors, is shown in FIG. 8. In FIG. 8, only connectors having correlation with p-values below 0.001/(n(n−1)/2) are shown. All metabolites with significant partial correlations to X-11593, at a significance level of 0.05, are listed in Table 18. In FIG. 8, broken line (dashed) connectors denote significant partial correlations between the connected metabolites while solid line (gray) connectors represent connections via a known biochemical reaction as provided by metabolic databases such as KEGG. The lines are weighted by p-value, the lower the p-value, the thicker the line. Both known metabolites directly associated with X-11593 belong to the ascorbate degradation pathway.

TABLE 18 Metabolites having significant partial correlation with the unknown metabolite X-11593. Unknown: Significant partial correlation to: p-value X-11593 X-01911 8.00E−38 ascorbate 6.59E−20 threonate 6.92E−20 X-12206 1.98E−11 1,5-anhydroglucitol (1,5-AG) 1.26E−07 C-glycosyltryptophan 6.80E−07

In the GWAS analysis, significant associations of X-11593 with SNPs in the gene encoding catechol-O-methyltransferase (COMT) were found. Table 19 shows the most significant hit from the GWAS analysis for X-11593. COMT is an enzyme relevant for the inactivation and degradation of many drugs. COMT O-methylates molecules with catechol like structures.

TABLE 19 Most significant hit in the GWAS for the unknown X-11593. Unknown Best Assoc. SNP Locus Enzyme p-value X-11593 rs4680 COMT catechol-O- 1.13E−48 methyltransferase

The constraints for X-11593 given by the GGM shown in FIG. 8 and given by the GWAS analysis were combined. According to the GWAS, X-11593 was probably a substrate or a product of O-methylation. The mass differences to the known metabolites neighboring X-11593, namely ascorbate and threonate, was determined. While the mass difference of X-11593 and threonate is 54, X-11593 and ascorbate show a mass difference of 14, which corresponds to the addition of a methyl moiety. These observations made O-methylated ascorbate derivatives good candidates for X-11593.

From an experimental perspective, this hypothesis was supported by the accurate neutral mass 190.04787 determined for X-11593. Based on the accurate mass, the molecular formula for X-11593 was determined to be C₇H₁₀O₆. In ChemSpider, 93 molecules were described for this formula. Out of the 93 molecules, three molecules represent O-methylated ascorbates. Their structures are shown in Formulas I, II, and III below. Two of the three molecules are methylated at one of the hydroxyl moieties of the 5-ring. The double bond within the 5-ring with its two hydroxyl moieties could “mimic” the corresponding planar substructure in catechol, on which catechol-o-methyltransferase (COMT) is usually working. As such, the molecules of Formulas I and II are more probable candidates for X-11593. Experimentally, the retention time of X-11593 showed a slight shift compared to the time for ascorbate. This shift matches the shift expected for adding a methyl group. Moreover, the primary fragment loss for X-11593 is 60, which is the same as that for ascorbate. The mass loss of 15, also seen for X-11593, is typical for phenols substituted with a —OH and —OCH₃. Thus, it was hypothesized that X-11593 is 2-O-methyl ascorbic acid.

Candidate Molecules for X-11593

TABLE 2 UNKNOWN ASSOCIATING SNP PVAL_SNP X01911 rs4680 5.80E−11 X01911 rs165656 3.65E−10 X01911 rs176533 6.39E−07 X01911 rs4633 3.95E−08 X02249 rs867212 1.502E−06  X02269 rs6583967 4.84E−07 X02973 rs12652460 2.164E−06  X03003 rs10879287 6.13E−07 X03056 rs4240520 3.03E−08 X03088 rs7329126 1.322E−06  X03090 rs4952293 9.08E−07 X03094 rs3741298 1.49E−09 X04357 rs1953661 3.804E−06  X04494 rs9365108 4.653E−06  X04495 rs7634246 1.246E−06  X04498 rs3848141 2.59E−07 X04499 rs17076477 1.15E−07 X04500 rs2920861 1.081E−06  X04515 rs7785988 1.082E−06  X05426 rs1881514 1.67E−07 X05491 rs16823855 6.43E−07 X05907 rs1635181 3.764E−06  X06126 rs353807 8.06E−07 X06226 rs12413935 4.09E−11 X06227 rs1695945 6.75E−07 X06246 rs2219008 5.56E−07 X06267 rs17138748 4.229E−06  X06307 rs9316180 4.93E−09 X06350 rs12028243 6.31E−07 X06351 rs13097461 9.91E−07 X07765 rs4687417 9.67E−07 X08402 rs4902250 1.18E−09 X08766 rs11621845 1.193E−06  X08988 rs1398806 9.03E−07 X09026 rs9320134 1.32E−07 X09108 rs10972022 1.514E−06  X09706 rs803422 3.64E−07 X09789 rs2588976 1.49E−07 X10346 rs6432834 1.764E−06  X10395 rs10497458 2.54E−08 X10419 rs10260816 2.78E−06 X10429 rs6767775 4.028E−06  X10500 rs16946189 1.76E−06 X10506 rs1284066 9.82E−07 X10510 rs11856508 2.85E−07 X10675 rs2279812 4.77E−07 X10810 rs9610927 1.122E−06  X11204 rs1545358 1.234E−05  X11244 rs296391 2.12E−26 X11244 rs17272617 5.87E−13 X11244 rs2910400 4.38E−07 X11244 rs2932766 6.42E−09 X11244 rs2972515 3.14E−07 X11244 rs3745752 2.10E−10 X11244 rs4427918 5.77E−07 X11247 rs2807872 7.14E−08 X11255 rs8179972 1.694E−06  X11261 rs1247499 8.16E−07 X11299 rs671938 2.372E−06  X11315 rs7782739 1.325E−06  X11317 rs4575635 8.02E−08 X11319 rs11118895 2.90E−07 X11327 rs7129081 7.47E−07 X11334 rs16833988 1.725E−06  X11374 rs457075 3.39E−07 X11381 rs11106542 1.355E−06  X11412 rs359980 3.19E−07 X11421 rs12134854 1.90E−27 X11421 rs1001160 7.58E−11 X11421 rs10873788 2.05E−18 X11421 rs11161510 3.86E−25 X11421 rs11161511 2.20E−25 X11421 rs11161620 2.71E−19 X11421 rs11163904 1.16E−09 X11421 rs11163924 2.62E−25 X11421 rs1146579 2.28E−22 X11421 rs11579752 2.03E−09 X11421 rs1159215 1.20E−10 X11421 rs12090849 1.00E−18 X11421 rs12123977 8.67E−26 X11421 rs12131344 9.98E−11 X11421 rs12140121 1.13E−17 X11421 rs1250876 5.65E−14 X11421 rs1251079 1.61E−13 X11421 rs1251551 2.25E−12 X11421 rs1251584 5.87E−10 X11421 rs1303870 9.88E−13 X11421 rs1498311 1.35E−10 X11421 rs1689271 9.29E−12 X11421 rs1694419 1.61E−16 X11421 rs17097780 3.05E−08 X11421 rs17647178 2.02E−11 X11421 rs17650138 3.97E−16 X11421 rs1770887 1.36E−15 X11421 rs1796812 1.62E−12 X11421 rs211718 6.38E−27 X11421 rs2792664 3.51E−11 X11421 rs3818855 1.16E−19 X11421 rs5745347 4.19E−11 X11421 rs6699682 8.66E−19 X11421 rs7516477 1.25E−13 X11421 rs7519526 1.51E−11 X11421 rs7547056 9.14E−15 X11421 rs8396 2.01E−07 X11422 rs2406278 2.70E−07 X11423 rs6429032 1.43E−06 X11437 rs7779508 2.761E−06  X11438 rs174456 3.052E−06  X11440 rs296391 1.69E−43 X11440 rs17272617 9.63E−24 X11440 rs2910400 1.55E−11 X11440 rs2932766 5.51E−14 X11440 rs2972515 1.60E−12 X11440 rs3745752 1.08E−15 X11440 rs4427918 2.68E−09 X11441 rs6742078 5.59E−30 X11441 rs887829 2.79E−29 X11441 rs10179091 2.27E−18 X11441 rs10197460 2.09E−19 X11441 rs10203853 4.62E−08 X11441 rs11695484 8.25E−23 X11441 rs11891311 7.16E−17 X11441 rs2602380 2.24E−07 X11441 rs2741021 1.23E−14 X11441 rs2741023 1.68E−09 X11441 rs2741045 3.61E−23 X11441 rs3755319 3.31E−20 X11441 rs3806596 1.35E−21 X11441 rs3806597 9.58E−22 X11441 rs4294999 7.55E−21 X11441 rs4663965 1.21E−22 X11441 rs6714634 1.33E−22 X11441 rs6715325 2.07E−14 X11441 rs6736508 1.91E−18 X11441 rs6744284 4.49E−23 X11441 rs6753320 9.87E−18 X11441 rs6759892 2.74E−18 X11441 rs6761246 8.01E−07 X11441 rs7563561 5.68E−18 X11441 rs7564935 3.93E−19 X11441 rs7608175 6.89E−18 X11442 rs6742078 1.19E−25 X11442 rs887829 2.18E−25 X11442 rs10179091 8.51E−18 X11442 rs10197460 4.41E−16 X11442 rs10203853 3.78E−08 X11442 rs11695484 2.27E−21 X11442 rs11891311 3.83E−16 X11442 rs2741021 3.39E−12 X11442 rs2741023 6.41E−08 X11442 rs2741045 2.68E−19 X11442 rs3755319 1.58E−19 X11442 rs3806596 8.71E−21 X11442 rs3806597 8.89E−21 X11442 rs4294999 8.97E−20 X11442 rs4663965 4.82E−22 X11442 rs6714634 2.18E−21 X11442 rs6715325 1.37E−15 X11442 rs6736508 1.77E−15 X11442 rs6744284 1.20E−20 X11442 rs6753320 1.99E−14 X11442 rs6759892 1.76E−15 X11442 rs7563561 8.52E−15 X11442 rs7564935 1.15E−18 X11442 rs7608175 7.05E−15 X11443 rs16845476 4.651E−06  X11444 rs12466713 3.295E−06  X11445 rs296391 7.91E−07 X11445 rs13358334 2.36E−12 X11445 rs4149056 5.26E−08 X11445 rs4149081 8.46E−08 X11445 rs10461715 4.34E−11 X11445 rs11045879 1.69E−07 X11445 rs11746242 2.09E−07 X11445 rs2039623 9.49E−07 X11445 rs3756669 4.32E−12 X11445 rs700176 4.63E−07 X11445 rs7715372 2.10E−07 X11445 rs852238 3.66E−08 X11450 rs17325782 1.745E−06  X11452 rs253444 1.837E−06  X11469 rs4712963 5.57E−08 X11470 rs879154 4.09E−07 X11478 rs16946426 4.40E−07 X11483 rs10505816 5.91E−08 X11485 rs17361212 5.50E−07 X11491 rs4149056 7.76E−08 X11497 rs17265949 1.60E−07 X11521 rs6082408 5.39E−07 X11529 rs4149056 3.28E−81 X11529 rs4149081 1.93E−71 X11529 rs10841753 1.30E−11 X11529 rs10841791 4.70E−08 X11529 rs11045818 7.26E−12 X11529 rs11045819 3.44E−11 X11529 rs11045821 4.57E−13 X11529 rs11045825 1.36E−12 X11529 rs11045872 6.72E−11 X11529 rs11045879 2.58E−72 X11529 rs11045907 5.73E−12 X11529 rs11045908 2.09E−11 X11529 rs11045913 1.58E−19 X11529 rs11045953 2.17E−07 X11529 rs12372067 3.29E−18 X11529 rs12372111 5.22E−18 X11529 rs12812279 6.55E−13 X11529 rs1463565 8.81E−13 X11529 rs16923647 8.73E−52 X11529 rs2007379 1.66E−07 X11529 rs2169969 5.65E−12 X11529 rs2196019 3.70E−07 X11529 rs2199766 7.47E−07 X11529 rs2291075 5.84E−21 X11529 rs2291076 2.88E−14 X11529 rs2417963 2.10E−15 X11529 rs2900476 6.55E−49 X11529 rs4148984 4.24E−07 X11529 rs4148987 2.62E−07 X11529 rs4148988 4.53E−08 X11529 rs4149035 3.31E−08 X11529 rs4149057 1.52E−37 X11529 rs4149058 1.88E−54 X11529 rs4149069 2.06E−15 X11529 rs4149076 2.31E−22 X11529 rs7313671  8.1E−08 X11529 rs7965567 1.04E−07 X11529 rs7966613 8.95E−20 X11529 rs7967303 4.47E−07 X11529 rs7975631 2.32E−07 X11529 rs852549 2.87E−07 X11529 rs919840 8.13E−07 X11529 rs999278 1.05E−13 X11530 rs6742078 2.12E−38 X11530 rs887829 2.30E−38 X11530 rs10179091 3.43E−23 X11530 rs10197460 3.15E−21 X11530 rs10203853 3.21E−10 X11530 rs10209214 2.33E−07 X11530 rs11695484 8.23E−33 X11530 rs11891311 2.51E−23 X11530 rs17864661 6.67E−07 X11530 rs2602379 4.21E−08 X11530 rs2602380 3.62E−08 X11530 rs2741021 1.58E−16 X11530 rs2741023 3.64E−10 X11530 rs2741045 4.14E−25 X11530 rs3755319 3.71E−27 X11530 rs3806596 1.78E−28 X11530 rs3806597 5.61E−29 X11530 rs4148328 1.05E−08 X11530 rs4294999 3.20E−27 X11530 rs4663965 1.86E−27 X11530 rs6714634 9.08E−32 X11530 rs6715325 1.74E−21 X11530 rs6736508 5.10E−19 X11530 rs6744284 2.02E−31 X11530 rs6753320 3.05E−17 X11530 rs6754100 4.35E−09 X11530 rs6759892 8.45E−19 X11530 rs6761246 3.97E−09 X11530 rs7563561 1.07E−19 X11530 rs7564935 4.07E−24 X11530 rs7587916 2.64E−09 X11530 rs7608175 1.79E−19 X11537 rs1529294 8.715E−06  X11538 rs4149056 1.35E−37 X11538 rs4149081 1.54E−34 X11538 rs10841753 2.01E−23 X11538 rs10841791 1.93E−12 X11538 rs11045512 2.53E−09 X11538 rs11045521 2.31E−09 X11538 rs11045598 4.60E−12 X11538 rs11045611 3.81E−12 X11538 rs11045721 6.32E−11 X11538 rs11045767 6.50E−15 X11538 rs11045773 2.87E−15 X11538 rs11045776 4.64E−11 X11538 rs11045787 3.60E−16 X11538 rs11045818 1.94E−28 X11538 rs11045819 5.99E−28 X11538 rs11045821 8.73E−28 X11538 rs11045825 2.02E−27 X11538 rs11045872 7.95E−25 X11538 rs11045879 1.06E−34 X11538 rs11045907 2.03E−20 X11538 rs11045908 9.50E−19 X11538 rs11045913 6.03E−20 X11538 rs11045953 2.09E−12 X11538 rs12366506 4.50E−12 X11538 rs12370666 1.53E−14 X11538 rs12370697 2.17E−14 X11538 rs12431442 2.87E−07 X11538 rs12812279 2.73E−25 X11538 rs16923647 1.32E−26 X11538 rs17328763 2.08E−16 X11538 rs2007379 4.40E−07 X11538 rs2169969 1.22E−25 X11538 rs2199766 1.67E−20 X11538 rs2857468 1.53E−09 X11538 rs2900476 1.05E−16 X11538 rs3794319 3.18E−10 X11538 rs4148984 5.56E−12 X11538 rs4148987 2.55E−10 X11538 rs4148988 1.80E−12 X11538 rs4149057 1.20E−14 X11538 rs4149058 7.42E−18 X11538 rs718545 8.84E−07 X11538 rs7313671 1.10E−12 X11538 rs7962263 3.66E−11 X11538 rs7965567 3.95E−07 X11538 rs7967303 1.78E−11 X11538 rs919840 4.67E−07 X11540 rs10798980 1.642E−06  X11546 rs17700286 2.58E−06 X11550 rs894282 3.692E−06  X11552 rs12512174 1.062E−06  X11568 rs10449290 3.549E−06  X11593 rs4680 1.13E−48 X11593 rs1034564 5.36E−08 X11593 rs1034565  3.2E−08 X11593 rs1110478 4.80E−08 X11593 rs1375450 1.68E−07 X11593 rs1544325 6.70E−23 X11593 rs1640299 2.96E−13 X11593 rs165656 1.16E−46 X11593 rs16982844 1.92E−08 X11593 rs17119998 1.62E−07 X11593 rs17120009 2.41E−07 X11593 rs175165 2.17E−11 X11593 rs175197 2.28E−07 X11593 rs175199 8.76E−08 X11593 rs175200 1.74E−08 X11593 rs2073746 1.53E−12 X11593 rs2266943 7.17E−07 X11593 rs385773 7.78E−09 X11593 rs397701 8.14E−07 X11593 rs404060 8.69E−07 X11593 rs4633 9.74E−35 X11593 rs4646312 3.79E−22 X11593 rs4646316 3.14E−08 X11593 rs5748489 1.05E−19 X11593 rs5993875 2.90E−14 X11593 rs5993883 4.85E−16 X11593 rs8185002 2.58E−09 X11593 rs885980 5.18E−09 X11593 rs9332377 3.24E−07 X11593 rs9605063 5.38E−07 X11593 rs9606240 6.32E−10 X11786 rs7251736 3.11E−07 X11787 rs6710438 2.95E−37 X11787 rs7598396 1.66E−35 X11787 rs6753344 7.28E−36 X11787 rs10190002 8.81E−21 X11787 rs10193032 9.46E−13 X11787 rs10496190 1.75E−21 X11787 rs1052161 4.01E−21 X11787 rs1052162 1.94E−34 X11787 rs1083922 4.81E−26 X11787 rs10865398 1.49E−23 X11787 rs11126417 1.16E−22 X11787 rs11688718 9.37E−23 X11787 rs11884776 5.89E−35 X11787 rs11894953 5.26E−19 X11787 rs12052539 7.17E−24 X11787 rs12478346 2.36E−18 X11787 rs12624267 6.27E−23 X11787 rs12713798 3.17E−21 X11787 rs13017182 1.61E−26 X11787 rs13384952 1.11E−33 X11787 rs13386124 1.06E−22 X11787 rs13391552 6.27E−36 X11787 rs1403284 2.15E−35 X11787 rs17008991 2.42E−12 X11787 rs17009372 1.19E−16 X11787 rs17110192 1.08E−21 X11787 rs17110321 7.30E−23 X11787 rs17349049 1.24E−14 X11787 rs17350188 5.20E−19 X11787 rs17434655 6.24E−17 X11787 rs1806683 7.03E−23 X11787 rs1918863 5.86E−32 X11787 rs2070581 1.52E−08 X11787 rs2421574 2.39E−25 X11787 rs2567603 3.10E−20 X11787 rs3813227 8.61E−34 X11787 rs3813230 1.01E−10 X11787 rs4086116 2.15E−22 X11787 rs4346412 1.06E−23 X11787 rs4852316 3.71E−24 X11787 rs4852939 4.06E−24 X11787 rs6546826 1.81E−20 X11787 rs6706409 2.24E−21 X11787 rs6720094 2.43E−20 X11787 rs6745480 3.60E−32 X11787 rs6746971 7.00E−21 X11787 rs6747145 6.05E−22 X11787 rs6755500 5.74E−20 X11787 rs6759452 3.55E−27 X11787 rs7560272 1.56E−21 X11787 rs7566315 2.61E−25 X11787 rs7574291 2.05E−18 X11787 rs7576824 2.15E−22 X11787 rs7585004 8.65E−32 X11787 rs7594485 3.44E−11 X11787 rs7598660 1.58E−24 X11787 rs7606947 9.58E−25 X11787 rs7607014 7.51E−34 X11787 rs7896133 3.97E−26 X11787 rs9332093 1.11E−17 X11787 rs9332245 7.09E−21 X11792 rs4253252 1.60E−10 X11792 rs2937754 4.69E−07 X11793 rs6742078 1.03E−22 X11793 rs887829 2.59E−26 X11793 rs10179091 2.39E−13 X11793 rs10197460 5.52E−11 X11793 rs11695484 1.60E−20 X11793 rs11891311 4.54E−16 X11793 rs2602379 2.53E−09 X11793 rs2602380 8.00E−09 X11793 rs2741021 3.90E−14 X11793 rs2741045 3.09E−14 X11793 rs3755319 2.23E−19 X11793 rs3806596 1.80E−20 X11793 rs3806597 1.24E−20 X11793 rs4294999 5.10E−20 X11793 rs4663965 2.87E−19 X11793 rs6714634 1.68E−19 X11793 rs6715325 9.73E−15 X11793 rs6736508 7.72E−15 X11793 rs6744284 3.48E−16 X11793 rs6753320 1.19E−13 X11793 rs6754100 4.67E−10 X11793 rs6759892 2.11E−12 X11793 rs6761246 1.76E−09 X11793 rs7563561 1.43E−14 X11793 rs7564935 2.82E−15 X11793 rs7587916 2.25E−09 X11793 rs7608175 9.54E−15 X11795 rs9506615 1.388E−06  X11799 rs358231 2.87E−17 X11799 rs10021978 8.96E−07 X11799 rs358253 1.31E−15 X11799 rs358260 1.20E−16 X11799 rs430976 3.75E−08 X11805 rs10475541 1.573E−06  X11809 rs17008568 8.73E−07 X11818 rs196676 1.31E−07 X11820 rs2298423 3.18E−07 X11826 rs7111693 4.508E−06  X11843 rs690526 8.88E−08 X11845 rs10895514 4.085E−06  X11847 rs2432626 1.433E−06  X11849 rs7227515 3.843E−06  X11850 rs2003334 3.816E−06  X11852 rs895900 1.696E−06  X11858 rs1849474 9.87E−08 X11859 rs196703 5.813E−06  X11876 rs13190556 1.773E−06  X11880 rs4149056 6.73E−07 X12013 rs10493639 4.277E−06  X12029 rs7555956 3.307E−06  X12038 rs913112 9.827E−06  X12039 rs4908527 1.03E−07 X12056 rs1345015 3.069E−06  X12063 rs4149056 5.22E−20 X12063 rs10242455 1.47E−45 X12063 rs4149081 1.43E−18 X12063 rs10953286 1.71E−16 X12063 rs11045818 1.25E−07 X12063 rs11045819 5.15E−07 X12063 rs11045821 3.14E−09 X12063 rs11045825 1.65E−09 X12063 rs11045872 2.51E−09 X12063 rs11045879 1.76E−18 X12063 rs11045907 2.03E−08 X12063 rs11045908 2.27E−09 X12063 rs11045913 4.90E−09 X12063 rs11045953 4.02E−09 X12063 rs11734 9.52E−21 X12063 rs11769698 1.65E−11 X12063 rs12705036 2.48E−07 X12063 rs12812279 5.25E−09 X12063 rs13239596 2.36E−07 X12063 rs13310534 7.56E−08 X12063 rs1357319 4.72E−26 X12063 rs16923647 1.97E−11 X12063 rs17161652 1.53E−08 X12063 rs17161662 3.29E−08 X12063 rs17161692 5.64E−32 X12063 rs17161698 1.12E−07 X12063 rs17161726 1.43E−22 X12063 rs1851426 5.10E−21 X12063 rs2003499 1.65E−10 X12063 rs2014764 2.52E−17 X12063 rs2072181 6.98E−17 X12063 rs2169969 2.47E−09 X12063 rs2222411 3.87E−28 X12063 rs2240384 2.46E−19 X12063 rs2293256 4.47E−21 X12063 rs2687079 2.05E−24 X12063 rs2687145 1.58E−18 X12063 rs2740565 2.76E−24 X12063 rs2740566 1.63E−17 X12063 rs2741872 8.43E−19 X12063 rs2900476 2.75E−10 X12063 rs3735453 2.05E−38 X12063 rs3764815 4.90E−10 X12063 rs3794319 1.25E−08 X12063 rs3800960 3.65E−08 X12063 rs41385645 3.29E−18 X12063 rs4149057 5.60E−09 X12063 rs4149058 5.18E−11 X12063 rs4236542 4.64E−10 X12063 rs4646453 1.61E−08 X12063 rs472660 1.48E−11 X12063 rs4729568 9.66E−08 X12063 rs4836309 6.32E−07 X12063 rs4836313 7.58E−07 X12063 rs501275 1.23E−10 X12063 rs642761 9.09E−09 X12063 rs6651108 3.12E−18 X12063 rs678040 1.85E−11 X12063 rs6945984 7.59E−23 X12063 rs6955490 5.99E−11 X12063 rs6957987 2.55E−13 X12063 rs6960432 8.59E−08 X12063 rs776746 6.86E−36 X12063 rs7778571 4.26E−08 X12063 rs7787830 2.31E−07 X12063 rs7793425 1.26E−07 X12063 rs7962263 6.97E−07 X12063 rs7967303 1.31E−08 X12063 rs952319 1.95E−14 X12063 rs9969217 8.25E−09 X12092 rs4488133  2.24E−281 X12092 rs1061135 1.07E−14 X12092 rs1061437  3.78E−133 X12092 rs10736129  2.87E−152 X12092 rs10883094 1.72E−96 X12092 rs11189513 1.43E−07 X12092 rs11189552 4.29E−29 X12092 rs11189559 2.78E−16 X12092 rs11189569 2.82E−16 X12092 rs11189577 3.61E−17 X12092 rs11189600  3.06E−158 X12092 rs11189602 3.39E−13 X12092 rs11189615 1.21E−07 X12092 rs11189628 8.24E−08 X12092 rs11599208 3.97E−09 X12092 rs11814584 3.43E−27 X12092 rs12763326  3.37E−118 X12092 rs17109634 3.74E−29 X12092 rs1739  8.89E−202 X12092 rs2095365 5.82E−07 X12092 rs2147897  3.86E−264 X12092 rs2147901  1.30E−117 X12092 rs2274248  3.53E−152 X12092 rs2296435  1.75E−121 X12092 rs2862297 1.26E−27 X12092 rs3830020  1.09E−205 X12092 rs4345897  1.20E−207 X12092 rs4400721  1.28E−139 X12092 rs4491153 5.90E−14 X12092 rs4551689  7.21E−188 X12092 rs4568939 1.65E−57 X12092 rs4611142 2.38E−08 X12092 rs4917817 2.99E−07 X12092 rs4917818 1.99E−12 X12092 rs4919209 2.65E−13 X12092 rs6584185 3.37E−13 X12092 rs6584206 7.82E−10 X12092 rs7072216  3.88E−275 X12092 rs7075856 8.63E−07 X12092 rs747022 4.86E−08 X12092 rs765456 1.45E−12 X12092 rs7894393 3.40E−15 X12092 rs7896828  6.99E−185 X12092 rs7907555  2.18E−229 X12092 rs7909297 3.64E−14 X12092 rs7914401  1.70E−123 X12092 rs7924303  1.34E−216 X12093 rs6710438 4.24E−18 X12093 rs4488133 1.35E−27 X12093 rs7598396 1.73E−19 X12093 rs6753344 1.39E−20 X12093 rs10190002 6.79E−11 X12093 rs10496190 1.61E−09 X12093 rs1052161 5.57E−10 X12093 rs1052162 5.40E−20 X12093 rs1061437 2.13E−18 X12093 rs10736129 5.47E−21 X12093 rs1083922 5.63E−12 X12093 rs10865398 2.71E−11 X12093 rs10883094 9.80E−15 X12093 rs11126417 1.68E−09 X12093 rs11189552 2.46E−07 X12093 rs11189600 5.78E−17 X12093 rs11688718 5.23E−11 X12093 rs11884776 6.14E−21 X12093 rs11894953 3.24E−08 X12093 rs12052539 1.58E−10 X12093 rs12478346 1.53E−11 X12093 rs12624267 9.37E−10 X12093 rs12631271 4.71E−07 X12093 rs12713798 8.29E−10 X12093 rs12763326 4.13E−16 X12093 rs13017182 1.32E−13 X12093 rs13384952 3.22E−18 X12093 rs13386124 7.74E−12 X12093 rs13391552 8.86E−22 X12093 rs1403284 2.44E−19 X12093 rs17008991 2.87E−07 X12093 rs17009372 7.86E−07 X12093 rs17022443 4.62E−08 X12093 rs17288261 4.89E−07 X12093 rs17349049 1.96E−08 X12093 rs17350188 2.21E−08 X12093 rs1739 1.53E−21 X12093 rs17434655 2.54E−08 X12093 rs17668735 4.04E−07 X12093 rs1806683 7.13E−10 X12093 rs1918863 6.59E−20 X12093 rs2147897 1.24E−26 X12093 rs2147901 4.00E−16 X12093 rs2274248 2.67E−22 X12093 rs2296435 1.29E−16 X12093 rs2421574 8.36E−12 X12093 rs2567603 2.87E−09 X12093 rs3813227 6.80E−18 X12093 rs3830020 2.10E−21 X12093 rs4345897 3.07E−20 X12093 rs4346412 6.52E−14 X12093 rs4400721 1.31E−17 X12093 rs4551689 9.79E−19 X12093 rs4568939 4.96E−10 X12093 rs4852316 5.44E−11 X12093 rs4852939 3.73E−11 X12093 rs6546826 8.00E−14 X12093 rs6706409 1.13E−13 X12093 rs6720094 7.43E−12 X12093 rs6745480 3.26E−17 X12093 rs6746971 3.15E−10 X12093 rs6747145 6.50E−11 X12093 rs6755500 9.22E−09 X12093 rs6759452 3.49E−13 X12093 rs6781351 2.08E−07 X12093 rs6782309 3.68E−07 X12093 rs7072216 5.69E−26 X12093 rs7560272 7.35E−12 X12093 rs7566315 2.47E−13 X12093 rs7574291 2.31E−11 X12093 rs7576824 4.68E−10 X12093 rs7585004 6.65E−17 X12093 rs7598660 8.72E−11 X12093 rs7606947 5.15E−12 X12093 rs7607014 2.58E−19 X12093 rs7896828 6.03E−19 X12093 rs7907555 2.72E−24 X12093 rs7914401 3.07E−17 X12093 rs7924303 2.44E−23 X12094 rs2596210 3.32E−07 X12095 rs10928512 2.002E−06  X12100 rs6151896 3.68E−07 X12116 rs704381 6.66E−07 X12188 rs10026884 5.586E−06  X12206 rs13416390 6.322E−06  X12212 rs4915559 7.43E−07 X12216 rs2736003 2.59E−06 X12217 rs1383950 4.48E−06 X12230 rs12504564 1.37E−07 X12231 rs2741110 5.064E−06  X12236 rs6083461 9.04E−07 X12244 rs10508017 2.88E−09 X12253 rs7936703 3.804E−06  X12261 rs2576810 6.29E−07 X12405 rs7564502 3.03E−07 X12407 rs1475525 8.07E−07 X12428 rs3205166 3.394E−06  X12441 rs138832 1.021E−06  X12442 rs2279502 8.86E−08 X12443 rs13256631 3.53E−07 X12450 rs11760020 5.31E−08 X12456 rs4149056 8.41E−17 X12456 rs4149081 3.77E−15 X12456 rs11045818 5.11E−08 X12456 rs11045819 1.41E−07 X12456 rs11045821 5.21E−07 X12456 rs11045825 9.32E−07 X12456 rs11045872 4.80E−07 X12456 rs11045879 2.13E−15 X12456 rs16837493 7.76E−07 X12456 rs16923647 1.98E−07 X12456 rs2169969 1.74E−07 X12456 rs2900476 6.42E−11 X12456 rs4149057 7.50E−08 X12456 rs4149058 2.93E−10 X12465 rs7723967 2.239E−06  X12510 rs6710438 2.06E−47 X12510 rs7598396 1.53E−56 X12510 rs6753344 1.78E−52 X12510 rs10190002 9.18E−27 X12510 rs10193032 5.74E−17 X12510 rs10496190 6.98E−25 X12510 rs1052161 1.15E−27 X12510 rs1052162 1.30E−54 X12510 rs1083922 1.56E−34 X12510 rs10865398 7.80E−31 X12510 rs11126417 2.72E−31 X12510 rs11688718 3.57E−27 X12510 rs11884776 3.15E−53 X12510 rs11894953 1.12E−25 X12510 rs12052539 7.17E−33 X12510 rs12467259 7.83E−07 X12510 rs12478346 4.31E−27 X12510 rs12624267 6.31E−31 X12510 rs12713798 3.38E−30 X12510 rs13017182 1.08E−37 X12510 rs13384952 6.74E−55 X12510 rs13386124 1.17E−31 X12510 rs13391552 1.47E−54 X12510 rs1403284 9.86E−55 X12510 rs17008991 8.76E−16 X12510 rs17009372 1.46E−21 X12510 rs17349049 2.55E−18 X12510 rs17350188 3.16E−27 X12510 rs17434655 2.92E−25 X12510 rs1806683 8.27E−30 X12510 rs1881244 2.31E−11 X12510 rs1918863 4.36E−52 X12510 rs2070581 5.16E−13 X12510 rs2421574 4.43E−34 X12510 rs2567603 2.21E−31 X12510 rs3813227 1.19E−50 X12510 rs3813230 1.36E−17 X12510 rs4346412 7.48E−32 X12510 rs4514898 2.68E−10 X12510 rs4852316 2.12E−33 X12510 rs4852939 7.79E−31 X12510 rs6546826 4.77E−31 X12510 rs6706409 2.47E−33 X12510 rs6720094 9.51E−31 X12510 rs6745480 3.83E−49 X12510 rs6746971 2.28E−25 X12510 rs6747145 1.91E−31 X12510 rs6755500 4.32E−28 X12510 rs6759452 2.67E−38 X12510 rs7560272 1.15E−30 X12510 rs7566315 6.61E−34 X12510 rs7570391 7.24E−09 X12510 rs7574291 4.52E−28 X12510 rs7576824 7.94E−29 X12510 rs7585004 4.20E−48 X12510 rs7594485 1.33E−16 X12510 rs7598660 7.32E−30 X12510 rs7606947 1.05E−33 X12510 rs7607014 7.94E−56 X12524 rs10497004 1.663E−06  X12544 rs798598 6.054E−06  X12556 rs1550642 6.63E−07 X12627 rs3798720 1.86E−07 X12644 rs6505683 4.49E−07 X12645 rs168190 4.09E−07 X12680 rs7477871 3.138E−06  X12696 rs1936074 6.65E−07 X12704 rs13129177 7.26E−07 X12711 rs11242244 3.60E−07 X12717 rs6695534 1.637E−06  X12719 rs11670870 1.147E−06  X12726 rs1015150 5.659E−06  X12728 rs11831314 2.252E−06  X12729 rs13246970 8.53E−08 X12734 rs12725733 1.48E−06 X12740 rs2301920 1.904E−06  X12749 rs6507247 5.04E−07 X12771 rs802441 6.40E−07 X12776 rs6429539 3.889E−06  X12786 rs17406291 7.00E−07 X12798 rs316020 1.73E−72 X12798 rs2448295 4.77E−34 X12798 rs2448298 2.36E−51 X12798 rs2619268 2.48E−14 X12798 rs315988 7.33E−41 X12798 rs316000 1.16E−25 X12798 rs316007 6.99E−38 X12798 rs316013 6.89E−38 X12798 rs316015 3.93E−35 X12798 rs316025 4.31E−22 X12798 rs316034 2.31E−27 X12798 rs316035 5.83E−33 X12798 rs316167 7.68E−09 X12798 rs316169 5.76E−09 X12798 rs316170 5.35E−09 X12798 rs384156 3.94E−08 X12798 rs393271 1.10E−08 X12798 rs409952 6.67E−07 X12798 rs410569 1.10E−08 X12798 rs415897 1.09E−08 X12798 rs435421  4.8E−07 X12798 rs446809 8.24E−09 X12798 rs505111 2.85E−09 X12798 rs515140 3.58E−36 X12798 rs533452 1.85E−11 X12798 rs596881 1.42E−57 X12798 rs667538 5.01E−09 X12816 rs13275783 1.704E−06  X12830 rs12517012 2.23E−07 X12844 rs465226 1.153E−06  X12847 rs9517904 4.538E−06  X12850 rs11019976 3.68E−08 X12851 rs11029926 4.43E−07 X12855 rs3820881 6.136E−06  X12990 rs2524299 9.74E−08 X13069 rs1958375 1.257E−06  X13183 rs10935295 3.21E−07 X13215 rs11880261 4.20E−08 X13372 rs1995973 1.996E−06  X13429 rs4149056 4.86E−22 X13429 rs4149081 6.57E−20 X13429 rs10841753 4.44E−09 X13429 rs11045512 1.13E−07 X13429 rs11045521 2.91E−07 X13429 rs11045773 3.51E−07 X13429 rs11045818 1.97E−09 X13429 rs11045819 5.94E−10 X13429 rs11045821 8.96E−11 X13429 rs11045825 3.79E−10 X13429 rs11045872 8.18E−10 X13429 rs11045879 1.04E−19 X13429 rs11045907 1.13E−09 X13429 rs11045908 4.64E−09 X13429 rs11045913 8.89E−12 X13429 rs12812279 1.11E−11 X13429 rs16923647 1.46E−11 X13429 rs2169969 6.42E−11 X13429 rs2900476 1.82E−16 X13429 rs4149057 5.36E−08 X13429 rs4149058 5.13E−15 X13431 rs2286963 2.68E−33 X13431 rs10932321 1.92E−15 X13431 rs11889646 2.09E−18 X13431 rs1396828 7.98E−16 X13431 rs1472955 8.82E−12 X13431 rs1509569 1.40E−24 X13431 rs2041688 8.58E−19 X13431 rs2539862 3.88E−14 X13431 rs2723222 7.77E−21 X13431 rs2723225 3.03E−22 X13431 rs3764913 6.93E−28 X13431 rs6725084 1.84E−16 X13431 rs6735154 9.39E−19 X13431 rs7557847 1.49E−27 X13431 rs7570090 2.16E−24 X13431 rs7583039 6.99E−19 X13431 rs7593548 2.29E−19 X13431 rs7596691 4.41E−12 X13435 rs2745454 8.22E−07 X13477 rs6753344 1.04E−07 X13496 rs1867237 1.255E−06  X13548 rs6882355 3.548E−06  X13549 rs17122693 2.68E−06 X13553 rs17135372 4.136E−06  X13619 rs1478903 6.588E−06  X13640 rs7716072 2.288E−06  X13671 rs4684510 6.78E−07 X13741 rs3014887 1.66E−06 X13859 rs17817518 4.137E−06  X14056 rs2224768 1.642E−06  X14057 rs6659821 3.606E−06  X14086 rs4351 4.02E−09 X14189 rs4351 2.39E−16 X14189 rs4343 1.48E−16 X14189 rs4325 2.27E−16 X14189 rs1029765 9.57E−07 X14189 rs12494751 6.98E−07 X14189 rs4293 4.56E−10 X14189 rs4324 2.70E−15 X14189 rs4329 8.58E−15 X14189 rs4968762 1.93E−07 X14189 rs558240 2.55E−07 X14189 rs6415419 2.44E−07 X14189 rs6468424 1.09E−07 X14189 rs651007 9.76E−08 X14205 rs4351 3.97E−14 X14205 rs4343 2.28E−13 X14205 rs4325 6.40E−13 X14205 rs4324 2.81E−11 X14205 rs4329 1.77E−10 X14205 rs4736744 8.70E−07 X14208 rs4351 4.58E−15 X14208 rs4343 1.92E−13 X14208 rs4325 2.61E−13 X14208 rs11190338 9.54E−07 X14208 rs17061987 3.11E−07 X14208 rs4293 1.92E−09 X14208 rs4324 7.82E−13 X14208 rs4329 7.23E−12 X14208 rs4895946 5.15E−07 X14208 rs4897621 9.22E−07 X14208 rs9375929 3.49E−07 X14304 rs4351 3.87E−12 X14304 rs4343 6.60E−12 X14304 rs4325 2.68E−12 X14304 rs4293 9.95E−08 X14304 rs4324 8.40E−12 X14304 rs4329 7.85E−11 X14304 rs8066722 7.87E−07 X14374 rs1374273 4.19E−07 X14450 rs644045 1.015E−05  X14473 rs7828363 1.86E−07 X14478 rs7239408 4.673E−06  X14486 rs10079220 1.79E−07 X14541 rs1026975 9.57E−07 X14588 rs6853408 7.24E−07 X14625 rs6558292 4.18E−09 X14626 rs4149056 2.91E−13 X14626 rs4149081 2.08E−13 X14626 rs10841753 1.02E−07 X14626 rs11045767  4.9E−07 X14626 rs11045787 5.70E−08 X14626 rs11045818 7.49E−10 X14626 rs11045819 5.60E−10 X14626 rs11045821 1.55E−09 X14626 rs11045825 2.76E−08 X14626 rs11045872 9.22E−10 X14626 rs11045879 4.19E−13 X14626 rs11045907 1.44E−09 X14626 rs11045908 1.23E−07 X14626 rs11045913 8.46E−11 X14626 rs12812279 8.14E−09 X14626 rs16923647 1.42E−08 X14626 rs2169969 3.39E−09 X14626 rs2900476 6.26E−11 X14626 rs4149058 4.05E−09 X14632 rs10484128 7.48E−07 X14658 rs11265831 1.221E−06  X14662 rs12093439 1.83E−07 X14663 rs7914737 2.42E−07 X14745 rs6560714 5.15E−07 X14977 rs16834673 1.163E−06 

TABLE 3 BEST ASSO- UN- CIATING KNOWN SIGNIF GGM CORR PARTNER PVAL GGM SNP LOCUS dbSNP DESC LOCUS PVAL SNP X01911 piperine 3.28E−118 rs4680 COMT catechol-O- 5.80E−011 X11847 2.41E−047 methyltransferase X11849 1.09E−038 X11593 2.98E−038 X11485 8.36E−010 X12206 4.77E−007 X02249 @carboxy4methyl5propyl- 5.23E−032 rs867212 GRAMD4 1.50E−006 2furanpropanoateCMPF eicosapentaenoateEPA205n3 9.64E−021 theobromine 7.53E−009 X02269 3.11E−008 X02269 X11469 0 rs6583967 CYP2C8 4.84E−007 @3carboxy4methyl5propyl- 2.04E−045 2furanpropanoateCMPF X02249 3.11E−008 X02973 erythrose 1.24E−017 rs12652460 2.16E−006 ascorbateVitaminC 5.26E−011 threonate 5.24E−010 X13619 2.09E−008 X04357 3.85E−007 X03003 X10810 1.66E−010 rs10879287 6.13E−007 erythrose 1.19E−009 X03056 X11422 1.55E−008 rs4240520 PAOX 3.03E−008 isobutyrylcarnitine 1.41E−007 citrulline 7.89E−007 X03088 arginine 6.36E−018 rs7329126 1.32E−006 citrulline 2.02E−009 phosphate 2.00E−007 X03090 rs4952293 9.08E−007 X03094 @2palmitoylglycerophosphocholine 4.97E−013 rs3741298 1.49E−009 cholesterol 1.49E−010 citrate 1.39E−008 @1stearoylglycerophosphoinositol 1.81E−007 X04357 X12786 9.02E−096 rs1953661 3.80E−006 erythrose 4.93E−019 fructose 5.26E−014 threonate 3.04E−010 threonine 8.87E−008 aspartate 1.10E−007 X02973 3.85E−007 @1palmitoylglycerophosphoinositol 6.32E−007 X04494 rs9365108 4.65E−006 X04495 @2aminobutyrate 2.85E−041 rs7634246 1.25E−006 creatine 1.45E−026 @2hydroxybutyrateAHB 2.84E−012 X12244 5.33E−012 X12556 4.26E−008 X13435 5.76E−008 X04498 X05426 5.71E−016 rs3848141 UNC13C 2.59E−007 @2hydroxyisobutyrate 7.67E−009 threitol 6.39E−008 urea 1.99E−007 X04499 X05491 2.48E−019 rs17076477 1.15E−007 X04500 rs2920861 1.08E−006 X04515 rs7785988 LOC100286906 1.08E−006 X05426 X12039 7.82E−037 rs1881514 SPON1 1.67E−007 X04498 5.71E−016 quinate 2.10E−007 X05491 X04499 2.48E−019 rs16823855 6.43E−007 X13372 2.81E−008 erythrose 1.34E−007 X05907 X10395 7.52E−048 rs1635181 THSD7A 3.76E−006 X06226 2.86E−008 X06126 pcresolsulfate 6.57E−074 rs353807 8.06E−007 threitol 4.94E−007 X06226 X10395 1.17E−013 rs12413935 NRG3 neuregulin 3 4.09E−011 X09026 2.58E−012 X05907 2.86E−008 X06227 acetylphosphate 4.28E−084 rs1695945 6.75E−007 X06246 alanine 2.20E−065 rs2219008 5.56E−007 X14977 1.46E−008 X06267 citrulline 1.25E−021 rs17138748 LHFPL3 4.23E−006 X10810 1.26E−007 X06307 X11805 4.70E−042 rs9316180 CPB2 4.93E−009 X14205 2.24E−016 DSGEGDFXAEGGGVR 1.36E−007 X12798 2.74E−007 X06350 rs12028243 6.31E−007 X06351 rs13097461 9.91E−007 X07765 rs4687417 ATP13A5 9.67E−007 X08402 X10510 7.78E−067 rs4902250 SYNE2 1.18E−009 X08766 rs11621845 CCDC88C 1.19E−006 X08988 glycine 2.40E−024 rs1398806 9.03E−007 alanine 1.46E−010 X09026 X10395 2.56E−012 rs9320134 1.32E−007 X06226 2.58E−012 X09108 glutamine 5.89E−012 rs10972022 UBAP1 1.51E−006 X09706 X13619 6.34E−112 rs803422 MTHFD1L 3.64E−007 urea 4.61E−111 @2hydroxyisobutyrate 1.61E−011 X09789 X12253 9.90E−025 rs2588976 1.49E−007 X12039 6.63E−011 @4vinylphenolsulfate 7.35E−011 homostachydrine 3.17E−010 X10346 rs6432834 CSRNP3 1.76E−006 X10395 X05907 7.52E−048 rs10497458 2.54E−008 X06226 1.17E−013 X09026 2.56E−012 ascorbateVitaminC 7.41E−007 X10419 cholesterol 2.70E−065 rs10260816 2.78E−006 X10510 6.57E−028 X10500 9.58E−017 phosphate 1.75E−009 acetylphosphate 1.74E−007 X10429 rs6767775 4.03E−006 X10500 cholesterol 1.67E−019 rs16946189 1.76E−006 X10419 9.58E−017 acetylphosphate 5.44E−008 X10506 glucose 3.69E−014 rs1284066 9.82E−007 lysine 4.32E−014 aspartate 1.22E−013 pyruvate 2.36E−010 X13619 5.15E−009 serine 2.14E−007 X10510 X08402 7.78E−067 rs11856508 2.85E−007 X10419 6.57E−028 X10675 rs2279812 4.77E−007 X10810 hypoxanthine 7.14E−014 rs9610927 1.12E−006 X03003 1.66E−010 X12990 5.78E−008 X06267 1.26E−007 ascorbateVitaminC 1.56E−007 cysteineglutathionedisulfide 1.98E−007 X12441 2.26E−007 X11204 X11327 3.22E−270 rs1545358 1.23E−005 bilirubinEZorZE 2.84E−009 X11809 2.98E−008 octanoylcarnitine 3.09E−007 X11244 X11443 8.93E−113 rs296391 SULT2A1; CRX sulfotransferase 2.12E−026 X11440 7.62E−093 family, cytosolic, dehydroisoandrosteronesulfateDHEAS 7.47E−037 2A, epiandrosteronesulfate 6.66E−016 dehydroepiandros thromboxaneB2 1.12E−012 terone (DHEA)- X11470 4.12E−009 preferring, member 1; cone- rod homeobox X11247 X11787 7.05E−007 rs2807872 7.14E−008 X11255 @4vinylphenolsulfate 4.99E−019 rs8179972 1.69E−006 @2methylbutyroylcarnitine 7.18E−019 eicosenoate201n9or11 1.39E−007 @4ethylphenylsulfate 6.60E−007 X11261 linolenatealphaorgamma183n3or6 7.91E−081 rs1247499 C10orf11 8.16E−007 @10undecenoate111n1 4.18E−008 isobutyrylcarnitine 1.31E−007 X11880 3.66E−007 X11299 rs671938 EYS 2.37E−006 X11315 rs7782739 1.33E−006 X11317 X11497 1.58E−032 rs4575635 KLK13 8.02E−008 X12038 1.49E−028 X12524 1.52E−010 X11319 margarate170 1.84E−015 rs11118895 2.90E−007 @3methoxytyrosine 2.14E−013 @10heptadecenoate171n7 3.11E−009 myristoleate141n5 1.04E−008 @10nonadecenoate191n9 1.94E−007 X11327 X11204 3.22E−270 rs7129081 7.47E−007 octanoylcarnitine 6.97E−007 X11334 pipecolate 5.50E−021 rs16833988 1.73E−006 indolelactate 1.52E−008 X11374 rs457075 3.39E−007 X11381 X12798 5.21E−010 rs11106542 CLLU1; CLLU1OS 1.36E−006 X11442 1.09E−007 @5oxoproline 1.45E−007 X11412 rs359980 3.19E−007 X11421 X13435 9.93E−056 rs12134854 ACADM acyl-CoA 1.90E−027 linoleate182n6 1.55E−045 dehydrogenase, octanoylcarnitine 1.05E−040 C-4 to C-12 hexanoylcarnitine 1.00E−023 straight chain X11422 xanthine 9.35E−110 rs2406278 DAB1 2.70E−007 urate 6.50E−018 hypoxanthine 2.81E−015 X03056 1.55E−008 X11423 X12749 2.88E−150 rs6429032 RYR2 1.43E−006 X11437 rs7779508 2.76E−006 X11438 @10undecenoate111n1 4.36E−042 rs174456 FADS3 3.05E−006 @2hydroxyisobutyrate 5.57E−011 @10nonadecenoate191n9 1.09E−009 X11847 1.66E−008 X11538 6.88E−007 X11440 X11244 7.62E−093 rs296391 SULT2A1; CRX sulfotransferase 1.69E−043 X11445 1.13E−045 family, cytosolic, X11470 5.45E−011 2A, epiandrosteronesulfate 5.89E−011 dehydroepiandrosterone X12844 2.73E−010 (DHEA)- preferring, member 1; cone- rod homeobox X11441 X11442 0 rs6742078 UGT1A1; UGT1A10; UDP 5.59E−030 X11530 1.18E−008 UGT1A3; UGT1A4; glucuronosyltransferase bilirubinZZ 2.35E−007 UGT1A5; UGT1A6; 1 family, UGT1A7; polypeptide A UGT1A8; UGT1A9 X11442 X11441 0 rs6742078 UGT1A1; UGT1A10; UDP 1.19E−025 X11530 4.41E−055 UGT1A3; UGT1A4; glucuronosyltransferase bilirubinZZ 3.02E−018 UGT1A5; UGT1A6; 1 family, X11381 1.09E−007 UGT1A7; polypeptide A UGT1A8; UGT1A9 X11443 X11244 8.93E−113 rs16845476 LRP1B 4.65E−006 dehydroisoandrosteronesulfateDHEAS 4.10E−106 epiandrosteronesulfate 6.06E−093 X11450 2.17E−037 X12844 3.73E−010 X11444 X11470 5.28E−088 rs12466713 BIRC6 3.30E−006 X12844 1.35E−048 cortisone 3.28E−007 taurolithocholate3sulfate 3.91E−007 X11445 X11440 1.13E−045 rs13358334 UGT3A1 UDP 2.36E−012 pyroglutamine 2.38E−009 glycosyltransferase 3 family, polypeptide A1 X11450 dehydroisoandrosteronesulfateDHEAS 1.11E−101 rs17325782 NCKAP5 1.75E−006 X11443 2.17E−037 epiandrosteronesulfate 1.61E−011 X11452 X12231 0 rs253444 1.84E−006 piperine 1.38E−031 X11469 X02269 0 rs4712963 5.57E−008 X11470 X11444 5.28E−088 rs879154 4.09E−007 cortisol 1.80E−019 X11440 5.45E−011 X11244 4.12E−009 @1oleoylglycerophosphocholine 1.29E−007 heme 3.11E−007 X11478 rs16946426 GPC5 4.40E−007 X11483 rs10505816 5.91E−008 X11485 X01911 8.36E−010 rs17361212 5.50E−007 X12231 3.80E−009 @1palmitoylglycerophosphocholine 4.10E−007 X11491 rs4149056 SLCO1B1 solute carrier 7.76E−008 organic anion transporter family, member 1B1 X11497 X11317 1.58E−032 rs17265949 NOSTRIN 1.60E−007 X14977 2.02E−009 pelargonate90 1.24E−007 X11521 rs6082408 5.39E−007 X11529 rs4149056 SLCO1B1 solute carrier 3.28E−081 organic anion transporter family, member 1B1 X11530 bilirubinZZ 8.97E−085 rs6742078 UGT1A1; UGT1A10; UDP 2.12E−038 X11442 4.41E−055 UGT1A3; UGT1A4; glucuronosyltransferase X11441 1.18E−008 UGT1A5; UGT1A6; 1 family, UGT1A7; polypeptide A UGT1A8; UGT1A9 X11537 X11540 0 rs1529294 CNTNAP5 8.72E−006 glucose 1.47E−007 X11538 octadecanedioate 7.60E−059 rs4149056 SLCO1B1 solute carrier 1.35E−037 X12253 4.01E−012 organic anion X12063 1.19E−011 transporter family, X11438 6.88E−007 member 1B1 X11540 X11537 0 rs10798980 1.64E−006 choline 2.06E−013 X14977 5.54E−007 X11546 rs17700286 2.58E−006 X11550 pelargonate90 9.78E−035 rs894282 3.69E−006 heme 3.89E−011 bilirubinEZorZE 6.24E−007 X11552 oleamide 8.95E−012 rs12512174 SORBS2 1.06E−006 X11568 rs10449290 PLD5 3.55E−006 X11593 X01911 2.98E−038 rs4680 COMT catechol-O- 1.13E−048 ascorbateVitaminC 6.59E−020 methyltransferase threonate 6.92E−020 X12206 1.98E−011 @15anhydroglucitol15AG 1.26E−007 Cglycosyltryptophan 7.68E−007 X11786 pipecolate 1.55E−021 rs7251736 LRRC68 3.11E−007 Nacetylornithine 8.11E−010 X11787 Nacetylornithine 8.24E−035 rs6710438 ALMS1 (NAT8) Alstrom syndrome 1 2.95E−037 X11247 7.05E−007 uridine 7.06E−007 X11792 rs4253252 KLKB1 kallikrein B, 1.60E−010 plasma (Fletcher factor) 1 X11793 bilirubinEE 8.57E−108 rs887829 UGT1A1; UGT1A10; UDP 2.59E−026 UGT1A3; UGT1A4; glucuronosyltransferase UGT1A5; UGT1A6; 1 family, UGT1A7; polypeptide A UGT1A8; UGT1A9 X11795 linolenatealphaorgamma183n3or6 4.74E−008 rs9506615 1.39E−006 X11799 stachydrine 1.14E−017 rs358231 GBA3 glucosidase, beta, 2.87E−017 scylloinositol 8.55E−011 acid 3 (cytosolic) X14086 6.47E−007 X11805 X14208 6.14E−062 rs10475541 1.57E−006 X14478 3.95E−055 X06307 4.70E−042 aspartylphenylalanine 1.39E−021 DSGEGDFXAEGGGVR 9.79E−021 X14205 1.49E−017 X14450 2.95E−011 X11809 bilirubinEE 2.60E−020 rs17008568 8.73E−007 cholesterol 1.74E−009 X11204 2.98E−008 stearoylcarnitine 1.59E−007 bilirubinZZ 1.86E−007 glycerophosphorylcholineGPC 2.51E−007 bilirubinEZorZE 3.37E−007 X11818 X12510 5.22E−039 rs196676 1.31E−007 linolenatealphaorgamma183n3or6 3.01E−007 linoleate182n6 3.83E−007 X11820 rs2298423 3.18E−007 X11826 rs7111693 4.51E−006 X11843 rs690526 WDR72 8.88E−008 X11845 rs10895514 4.09E−006 X11847 X11849 0 rs2432626 SNX29 1.43E−006 X01911 2.41E−047 X12231 1.91E−013 X11438 1.66E−008 @4ethylphenylsulfate 6.53E−007 X11849 X11847 0 rs7227515 THOC1 3.84E−006 X01911 1.09E−038 X12231 1.91E−008 @1stearoylglycerophosphoinositol 1.03E−007 X11850 rs2003334 SLC41A3 3.82E−006 X11852 rs895900 FREM2 1.70E−006 X11858 rs1849474 9.87E−008 X11859 pelargonate90 1.12E−067 rs196703 5.81E−006 X11876 rs13190556 1.77E−006 X11880 X11261 3.66E−007 rs4149056 SLCO1B1 solute carrier 6.73E−007 eicosapentaenoateEPA205n3 4.36E−007 organic anion transporter family, member 1B1 X12013 rs10493639 4.28E−006 X12029 X14588 2.05E−026 rs7555956 SPATA17 3.31E−006 X12038 X11317 1.49E−028 rs913112 9.83E−006 X12524 1.10E−016 cholesterol 3.50E−008 X13372 6.56E−008 X12039 quinate 3.05E−054 rs4908527 1.03E−007 X05426 7.82E−037 caffeine 1.07E−018 X12217 2.63E−014 X14473 2.14E−011 X09789 6.63E−011 @3methoxytyrosine 3.43E−009 theophylline 2.32E−008 piperine 1.01E−007 X12056 rs1345015 3.07E−006 X12063 thromboxaneB2 1.08E−015 rs10242455 CYP3A5 1.47E−045 dehydroisoandrosteronesulfateDHEAS 3.96E−013 X11538 1.19E−011 @7alphahydroxy3oxo4cholestenoate7Hoca 1.13E−007 X12092 rs4488133 PYROXD2 pyridine 2.24E−281 nucleotide- disulphide oxidoreductase domain 2 X12093 rs4488133 PYROXD2 pyridine 1.35E−027 nucleotide- disulphide oxidoreductase domain 2 X12094 X12095 0 rs2596210 RYR3 3.32E−007 X12095 X12094 0 rs10928512 TMEM163 2.00E−006 X12100 kynurenine 2.02E−033 rs6151896 MSH3 3.68E−007 X12116 rs704381 PRICKLE2 6.66E−007 X12188 rs10026884 GABRB1 5.59E−006 X12206 X11593 1.98E−011 rs13416390 LRRTM4 6.32E−006 X01911 4.77E−007 X12212 rs4915559 CFHR4 7.43E−007 X12216 rs2736003 2.59E−006 X12217 catecholsulfate 5.67E−185 rs1383950 CSMD1 4.48E−006 X12039 2.63E−014 X12230 rs12504564 TMEM144 1.37E−007 X12231 X11452 0 rs2741110 5.06E−006 X11847 1.91E−013 X11485 3.80E−009 X11849 1.91E−008 @3methyl2oxovalerate 2.30E−007 X12236 rs6083461 ZNF343 9.04E−007 X12244 X04495 5.33E−012 rs10508017 ABCC4 2.88E−009 creatinine 2.54E−007 X12253 X09789 9.90E−025 rs7936703 KCNQ1 3.80E−006 X11538 4.01E−012 betaine 2.93E−007 X12261 rs2576810 PTH2R 6.29E−007 X12405 @3indoxylsulfate 0 rs7564502 LRP1B 3.03E−007 DSGEGDFXAEGGGVR 2.64E−007 @2hydroxypalmitate 6.66E−007 X12407 rs1475525 DAPK1 8.07E−007 X12428 rs3205166 DDX58 3.39E−006 X12441 arachidonate204n6 1.52E−116 rs138832 BRD1 1.02E−006 @1arachidonoylglycerophosphocholine 9.39E−013 docosahexaenoateDHA226n3 2.74E−009 X10810 2.26E−007 dihomolinolenate203n3orn6 6.48E−007 X12442 X13069 5.73E−026 rs2279502 8.86E−008 @5dodecenoate121n7 3.91E−025 myristoleate141n5 1.04E−016 linoleate182n6 1.84E−016 X13431 5.43E−012 laurylcarnitine 1.78E−008 dihomolinoleate202n6 2.14E−008 hypoxanthine 2.26E−008 myristate140 7.54E−008 @2tetradecenoylcarnitine 3.61E−007 X12443 rs13256631 RGS22 3.53E−007 X12450 rs11760020 BMP6 5.31E−008 X12456 rs4149056 SLCO1B1 solute carrier 8.41E−017 organic anion transporter family, member 1B1 X12465 rs7723967 2.24E−006 X12510 X11818 5.22E−039 rs7598396 ALMS1 (NAT8?) Alstrom syndrome 1 1.53E−056 Nacetylornithine 1.25E−037 @10undecenoate111n1 6.15E−008 X12524 X12038 1.10E−016 rs10497004 1.66E−006 X11317 1.52E−010 palmitate160 1.84E−009 X13859 1.08E−007 X12544 rs798598 6.05E−006 X12556 threonine 4.59E−019 rs1550642 6.63E−007 X04495 4.26E−008 X12627 eicosenoate201n9or11 2.05E−007 rs3798720 ELOVL2 1.86E−007 X12644 @1arachidonoylglycerophospho- 1.11E−154 rs6505683 4.49E−007 ethanolamine @1docosahexaenoylglycerophosphocholine 2.13E−036 docosahexaenoateDHA226n3 3.89E−020 @1linoleoylglycerophosphoethanolamine 1.29E−007 X12645 rs168190 4.09E−007 X12680 rs7477871 PARD3 3.14E−006 X12696 @15anhydroglucitol15AG 7.82E−174 rs1936074 6.65E−007 X12704 rs13129177 7.26E−007 X12711 rs11242244 3.60E−007 X12717 rs6695534 SSBP3 1.64E−006 X12719 rs11670870 1.15E−006 X12726 rs1015150 TFEB 5.66E−006 X12728 rs11831314 2.25E−006 X12729 rs13246970 SDK1 8.53E−008 X12734 rs12725733 1.48E−006 X12740 rs2301920 CARD11 1.90E−006 X12749 X11423 2.88E−150 rs6507247 5.04E−007 X12771 rs802441 6.40E−007 X12776 X13619 4.14E−007 rs6429539 3.89E−006 X12786 X04357 9.02E−096 rs17406291 7.00E−007 lactate 1.06E−008 aspartate 4.79E−007 X12798 @dehydrocarnitine 1.95E−062 rs316020 SLC22A2 solute carrier 1.73E−072 X11381 5.21E−010 family 22 (organic X06307 2.74E−007 cation transporter), member 2 X12816 rs13275783 1.70E−006 X12830 rs12517012 2.23E−007 X12844 X11444 1.35E−048 rs465226 SLC35F1 1.15E−006 X11440 2.73E−010 X11443 3.73E−010 thromboxaneB2 1.48E−009 dehydroisoandrosteronesulfateDHEAS 2.01E−008 epiandrosteronesulfate 1.72E−007 X12847 rs9517904 CLYBL 4.54E−006 X12850 rs11019976 FAT3 3.68E−008 X12851 rs11029926 4.43E−007 X12855 rs3820881 SPATS2L 6.14E−006 X12990 docosahexaenoateDHA226n3 4.56E−037 rs2524299 FADS2 9.74E−008 eicosapentaenoateEPA205n3 1.41E−023 @3carboxy4methyl5propyl- 2.60E−021 2furanpropanoateCMPF dihomolinolenate203n3orn6 6.15E−020 @1arachidonoylglycerophosphocholine 1.21E−017 X10810 5.78E−008 adrenate224n6 9.70E−008 docosapentaenoaten3DPA225n3 1.21E−007 arachidonate204n6 1.98E−007 X13069 X12442 5.73E−026 rs1958375 1.26E−006 X13183 linoleamide182n6 3.36E−044 rs10935295 3.21E−007 @2stearoylglycerophosphocholine 5.89E−008 oleamide 1.74E−007 X13215 rs11880261 AKT2 4.20E−008 X13372 X05491 2.81E−008 rs1995973 2.00E−006 X12038 6.56E−008 bilirubinEZorZE 9.56E−008 @4ethylphenylsulfate 6.70E−007 X13429 rs4149056 SLCO1B1 solute carrier 4.86E−022 organic anion transporter family, member 1B1 X13431 @10undecenoate111n1 1.82E−014 rs2286963 ACADL acyl-CoA 2.68E−033 @2methylbutyroylcarnitine 6.68E−013 dehydrogenase, X12442 5.43E−012 long chain @1palmitoleoylglycerophosphocholine 2.75E−007 X13435 X11421 9.93E−056 rs2745454 C6orf146 8.22E−007 acetylcarnitine 2.03E−017 @2tetradecenoylcarnitine 3.89E−012 hexanoylcarnitine 1.11E−010 X04495 5.76E−008 X13477 Nacetylornithine 6.71E−034 rs6753344 ALMS1 (NAT8?) Alstrom syndrome 1 1.04E−007 X13496 erythrose 3.87E−008 rs1867237 GOSR2 1.26E−006 X13548 X13549 1.51E−094 rs6882355 EFNA5 3.55E−006 X13549 X13548 1.51E−094 rs17122693 ATL1 2.68E−006 X13553 rs17135372 MCC 4.14E−006 X13619 X09706 6.34E−112 rs1478903 6.59E−006 urea 3.71E−036 asparagine 2.10E−011 X10506 5.15E−009 X02973 2.09E−008 X12776 4.14E−007 X13640 rs7716072 FSTL4 2.29E−006 X13671 rs4684510 6.78E−007 X13741 rs3014887 1.66E−006 X13859 X14625 3.30E−032 rs17817518 RYR3 4.14E−006 X12524 1.08E−007 X14056 X14057 9.43E−141 rs2224768 1.64E−006 bilirubinEE 7.85E−017 X14057 X14056 9.43E−141 rs6659821 3.61E−006 X14086 stachydrine 4.76E−058 rs4351 ACE angiotensin I 4.02E−009 X14304 1.54E−027 converting X14189 1.21E−025 enzyme (peptidyl- X14208 1.83E−014 dipeptidase A) 1 X14205 1.34E−012 @15anhydroglucitol15AG 3.03E−008 DSGEGDFXAEGGGVR 5.77E−007 X11799 6.47E−007 X14189 X14304 1.45E−204 rs4343 ACE angiotensin I 1.48E−016 X14086 1.21E−025 converting aspartylphenylalanine 3.74E−016 enzyme (peptidyl- DSGEGDFXAEGGGVR 3.05E−008 dipeptidase A) 1 X14205 X14478 4.22E−087 rs4351 ACE angiotensin I 3.97E−014 DSGEGDFXAEGGGVR 1.89E−035 converting cysteineglutathionedisulfide 9.59E−033 enzyme (peptidyl- X14208 2.25E−024 dipeptidase A) 1 X11805 1.49E−017 X06307 2.24E−016 X14086 1.34E−012 X14450 1.73E−012 ADSGEGDFXAEGGGVR 3.29E−009 aspartate 6.52E−009 phenylalanine 3.18E−007 glutamate 4.19E−007 ADpSGEGDFXAEGGGVR 6.26E−007 X14208 X14478 4.67E−153 rs4351 ACE angiotensin I 4.58E−015 X11805 6.14E−062 converting X14205 2.25E−024 enzyme (peptidyl- X14086 1.83E−014 dipeptidase A) 1 lysine 5.68E−011 X14304 X14189 1.45E−204 rs4325 ACE angiotensin I 2.68E−012 X14086 1.54E−027 converting enzyme (peptidyl- dipeptidase A) 1 X14374 X14473 2.57E−098 rs1374273 4.19E−007 theobromine 8.53E−045 hippurate 6.51E−017 quinate 3.66E−007 X14450 aspartylphenylalanine 5.43E−067 rs644045 1.02E−005 X14478 4.67E−014 X14205 1.73E−012 X11805 2.95E−011 X14473 X14374 2.57E−098 rs7828363 1.86E−007 quinate 2.66E−017 X12039 2.14E−011 theophylline 1.13E−008 bradykinindesarg9 5.15E−007 X14478 X14208 4.67E−153 rs7239408 4.67E−006 X14205 4.22E−087 X11805 3.95E−055 X14450 4.67E−014 cysteineglutathionedisulfide 5.67E−014 aspartylphenylalanine 3.51E−010 X14486 rs10079220 1.79E−007 X14541 rs1026975 ANK2 ankyrin 2, 9.57E−007 neuronal X14588 X12029 2.05E−026 rs6853408 CCDC158 7.24E−007 pipecolate 2.88E−013 histidine 1.29E−007 X14625 X13859 3.30E−032 rs6558292 OPLAH 4.18E−009 @5oxoproline 8.59E−028 glucose 3.49E−018 X14626 rs4149081 SLCO1B1 solute carrier 2.08E−013 organic anion transporter family, member 1B1 X14632 rs10484128 7.48E−007 X14658 rs11265831 1.22E−006 X14662 rs12093439 1.83E−007 X14663 rs7914737 2.42E−007 X14745 rs6560714 5.15E−007 X14977 X11497 2.02E−009 rs16834673 1.16E−006 X06246 1.46E−008 X11540 5.54E−007 

The invention claimed is:
 1. A method of determining the structure of an unknown metabolite when the accurate mass of the metabolite is known, comprising: (a) measuring amounts of known and unknown metabolites in subjects; (b) associating an unknown metabolite with a specific gene using a genome wide association study; (c) determining a protein associated with the specific gene and analyzing information for the protein; (d) associating the unknown metabolite with concentrations and/or ratios of other metabolites in the subjects using a partial correlation network; (e) obtaining chemical structural data for the unknown metabolite using a mass spectrometer; and (f) correlating the results obtained from steps (a) through (e) to determine the structure of the unknown metabolite.
 2. The method of claim 1, wherein the specific gene comprises a genetic polymorphism.
 3. The method of claim 1, further comprising reviewing the structure and/or characteristics of other metabolites associated with the specific gene from the genome wide association study and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the specific gene are involved prior to performing step (f).
 4. The method of claim 1, further comprising reviewing the structure and/or characteristics of the other metabolites associated with the unknown metabolite using the partial correlation network and/or identifying the biochemical pathway with which at least a portion of the other metabolites are involved prior to performing step (f).
 5. The method of claim 1, wherein the mass spectrometric data of the unknown metabolite comprises mass, molecular formula, or fragmentation spectra.
 6. The method of claim 1, wherein the information concerning the protein known to be associated with the gene includes function of the protein.
 7. The method of claim 1, wherein the protein performs a metabolic function.
 8. The method of claim 1, wherein the protein is an enzyme.
 9. The method of claim 8, wherein the substrate of the enzyme is identified.
 10. The method of claim 9, wherein the information includes the biochemical pathway for the substrate.
 11. The method of claim 9, wherein the information includes alternative biochemical pathways for the substrate.
 12. The method of claim 8, wherein an alternative substrate of the enzyme is determined.
 13. The method of claim 12, wherein the information includes the biochemical pathway for the substrate.
 14. The method of claim 1, wherein the protein is a transporter.
 15. The method of claim 3, wherein reviewing the structure and/or characteristics of the other metabolites associated with the specific gene from the genome wide association study and/or metabolites associated using the partial correlation network includes reviewing mass, class of compound, retention time, isotope patterns, fragments, and functionality of the other metabolites.
 16. The method of claim 1, wherein the association between the protein and the specific gene is the protein being encoded by the gene.
 17. A method of determining the structure of an unknown metabolite when the accurate mass of the metabolite is known, comprising: (a) measuring amounts of known and unknown metabolites in subjects; (b) associating an unknown metabolite with a specific gene from a genome wide association study; (c) determining a protein associated with the specific gene and analyzing information for the protein; (d) reviewing the structure and/or characteristics of other metabolites associated with the specific gene from the genome wide association study; and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the specific gene are involved; (e) obtaining chemical structural data for the unknown metabolite using a mass spectrometer; and (f) correlating results obtained from steps (a) through (e) to determine the structure of the unknown metabolite.
 18. A method of determining the structure of an unknown metabolite when the accurate mass of the metabolite is known, comprising: (a) measuring amounts of known and unknown metabolites in subjects; (b) associating an unknown metabolite with concentrations and/or ratios of other metabolites in the subjects using a partial correlation network; (c) reviewing the structure and/or characteristics of the other metabolites associated with the unknown metabolite; and/or identifying the biochemical pathway with which at least a portion of the other metabolites associated with the unknown metabolite are involved; (d) obtaining chemical structural data for the unknown metabolite using a mass spectrometer; and (e) correlating the results obtained from steps (a) through (d) to determine the structure of the unknown metabolite.
 19. The method of claim 18, further comprising associating the unknown metabolite with a specific gene from a genome wide association study and determining a protein associated with the specific gene and analyzing information for the protein. 